2001¶
In [1]:
import pandas as pd
import numpy as np
In [2]:
%matplotlib inline
from matplotlib import pyplot as plt
In [3]:
suicide_x = pd.read_csv(r"D:\Downloads\suicides_in_india.csv")
In [4]:
suicide_df= pd.DataFrame(suicide_x)
suicide_df
Out[4]:
In [5]:
df_2001 = suicide_df.loc[(suicide_df["Year"]==2001)]
df_2001
Out[5]:
In [6]:
t2001_dcount = df_2001.loc[:,"Total"]
s1_1 = np.sum(t2001_dcount)
s1_1
Out[6]:
In [7]:
male_2001 = df_2001.loc[(df_2001["Gender"]=="Male")]
male_2001
Out[7]:
In [8]:
m2001_count = male_2001.loc[:,"Total"]
s1_2 = np.sum(m2001_count)
s1_2
Out[8]:
In [9]:
female_2001 = df_2001.loc[(df_2001["Gender"]=="Female")]
female_2001
Out[9]:
In [10]:
fm2001_count = female_2001.loc[:,"Total"]
s1_3 = np.sum(fm2001_count)
s1_3
Out[10]:
In [11]:
df_age_d014_2001 = df_2001.loc[(df_2001["Age_group"]=="0-14")]
df_age_d014_2001
Out[11]:
In [12]:
t_014_2001 = df_age_d014_2001.loc[:,"Total"]
s1_4 = np.sum(t_014_2001)
s1_4
Out[12]:
In [13]:
df_age_d1529_2001 = df_2001.loc[(df_2001["Age_group"]=="15-29")]
df_age_d1529_2001
Out[13]:
In [14]:
t_1529_2001 = df_age_d1529_2001.loc[:,"Total"]
s1_5 = np.sum(t_1529_2001)
s1_5
Out[14]:
In [15]:
df_age_d3044_2001 = df_2001.loc[(df_2001["Age_group"]=="30-44")]
df_age_d3044_2001
Out[15]:
In [16]:
t_3044_2001 = df_age_d3044_2001.loc[:,"Total"]
s1_6 = np.sum(t_3044_2001)
s1_6
Out[16]:
In [17]:
df_age_d4559_2001 = df_2001.loc[(df_2001["Age_group"]=="45-59")]
df_age_d4559_2001
Out[17]:
In [18]:
t_4559_2001 = df_age_d4559_2001.loc[:,"Total"]
s1_7 = np.sum(t_4559_2001)
s1_7
Out[18]:
In [19]:
df_age_d60_2001 = df_2001.loc[(df_2001["Age_group"]=="60+")]
df_age_d60_2001
Out[19]:
In [20]:
t_60_2001 = df_age_d60_2001.loc[:,"Total"]
s1_8 = np.sum(t_60_2001)
s1_8
Out[20]:
In [21]:
df_age_d0100_2001 = df_2001.loc[(df_2001["Age_group"]=="0-100+")]
df_age_d0100_2001
Out[21]:
In [22]:
t_0100_2001 = df_age_d0100_2001.loc[:,"Total"]
s1_54 = np.sum(t_0100_2001)
s1_54
Out[22]:
In [23]:
df_014m_2001 = df_age_d014_2001.loc[(df_age_d014_2001["Gender"]=="Male")]
df_014m_2001
Out[23]:
In [24]:
t_014m_2001 = df_014m_2001.loc[:,"Total"]
s1_9 = np.sum(t_014m_2001)
s1_9
Out[24]:
In [25]:
df_014fm_2001 = df_age_d014_2001.loc[(df_age_d014_2001["Gender"]=="Female")]
df_014fm_2001
Out[25]:
In [26]:
t_014fm_2001 = df_014fm_2001.loc[:,"Total"]
s1_10 = np.sum(t_014fm_2001)
s1_10
Out[26]:
In [27]:
df_1529m_2001 = df_age_d1529_2001.loc[(df_age_d1529_2001["Gender"]=="Male")]
df_1529m_2001
Out[27]:
In [28]:
t_1529m_2001 = df_1529m_2001.loc[:,"Total"]
s1_11 = np.sum(t_1529m_2001)
s1_11
Out[28]:
In [29]:
df_1529fm_2001 = df_age_d1529_2001.loc[(df_age_d1529_2001["Gender"]=="Female")]
df_1529fm_2001
Out[29]:
In [30]:
t_1529fm_2001 = df_1529fm_2001.loc[:,"Total"]
s1_12 = np.sum(t_1529fm_2001)
s1_12
Out[30]:
In [31]:
df_3044m_2001 = df_age_d3044_2001.loc[(df_age_d3044_2001["Gender"]=="Male")]
df_3044m_2001
Out[31]:
In [32]:
t_3044m_2001 = df_3044m_2001.loc[:,"Total"]
s1_13 = np.sum(t_3044m_2001)
s1_13
Out[32]:
In [33]:
df_3044fm_2001 = df_age_d3044_2001.loc[(df_age_d3044_2001["Gender"]=="Female")]
df_3044fm_2001
Out[33]:
In [34]:
t_3044fm_2001 = df_3044fm_2001.loc[:,"Total"]
s1_14 = np.sum(t_3044fm_2001)
s1_14
Out[34]:
In [35]:
df_4559m_2001 = df_age_d4559_2001.loc[(df_age_d4559_2001["Gender"]=="Male")]
df_4559m_2001
Out[35]:
In [36]:
t_4559m_2001 = df_4559m_2001.loc[:,"Total"]
s1_15 = np.sum(t_4559m_2001)
s1_15
Out[36]:
In [37]:
df_4559fm_2001 = df_age_d4559_2001.loc[(df_age_d4559_2001["Gender"]=="Female")]
df_4559fm_2001
Out[37]:
In [38]:
t_4559fm_2001 = df_4559fm_2001.loc[:,"Total"]
s1_16 = np.sum(t_4559fm_2001)
s1_16
Out[38]:
In [39]:
df_60m_2001 = df_age_d60_2001.loc[(df_age_d60_2001["Gender"]=="Male")]
df_60m_2001
Out[39]:
In [40]:
t_60m_2001 = df_60m_2001.loc[:,"Total"]
s1_17 = np.sum(t_60m_2001)
s1_17
Out[40]:
In [41]:
df_60fm_2001 = df_age_d60_2001.loc[(df_age_d60_2001["Gender"]=="Female")]
df_60fm_2001
Out[41]:
In [42]:
t_60fm_2001 = df_60fm_2001.loc[:,"Total"]
s1_18 = np.sum(t_60fm_2001)
s1_18
Out[42]:
In [43]:
df_0100fm_2001 = df_age_d0100_2001.loc[(df_age_d0100_2001["Gender"]=="Female")]
df_0100fm_2001
Out[43]:
In [44]:
t_0100fm_2001 = df_0100fm_2001.loc[:,"Total"]
s1_55 = np.sum(t_0100fm_2001)
s1_55
Out[44]:
In [45]:
df_0100m_2001 = df_age_d0100_2001.loc[(df_age_d0100_2001["Gender"]=="Male")]
df_0100m_2001
Out[45]:
In [46]:
t_0100m_2001 = df_0100m_2001.loc[:,"Total"]
s1_56 = np.sum(t_0100m_2001)
s1_56
Out[46]:
In [47]:
df_st_AN_2001 = df_2001.loc[(df_2001["State"]=="A & N ISLANDS")]
df_st_AN_2001
Out[47]:
In [48]:
t_AN_2001 = df_st_AN_2001.loc[:,"Total"]
s1_19 = np.sum(t_AN_2001)
s1_19
Out[48]:
In [49]:
df_st_AP_2001 = df_2001.loc[(df_2001["State"]=="ANDHRA PRADESH")]
df_st_AP_2001
Out[49]:
In [50]:
t_AP_2001 = df_st_AP_2001.loc[:,"Total"]
s1_20 = np.sum(t_AP_2001)
s1_20
Out[50]:
In [51]:
df_st_ARP_2001 = df_2001.loc[(df_2001["State"]=="ARUNACHAL PRADESH")]
df_st_ARP_2001
Out[51]:
In [52]:
t_ARP_2001 = df_st_ARP_2001.loc[:,"Total"]
s1_21 = np.sum(t_ARP_2001)
s1_21
Out[52]:
In [53]:
df_st_AS_2001 = df_2001.loc[(df_2001["State"]=="ASSAM")]
df_st_AS_2001
Out[53]:
In [54]:
t_AS_2001 = df_st_AS_2001.loc[:,"Total"]
s1_22 = np.sum(t_AS_2001)
s1_22
Out[54]:
In [55]:
df_st_BH_2001 = df_2001.loc[(df_2001["State"]=="BIHAR")]
df_st_BH_2001
Out[55]:
In [56]:
t_BH_2001 = df_st_BH_2001.loc[:,"Total"]
s1_23 = np.sum(t_BH_2001)
s1_23
Out[56]:
In [57]:
df_st_CH_2001 = df_2001.loc[(df_2001["State"]=="CHANDIGARH")]
df_st_CH_2001
Out[57]:
In [58]:
t_CH_2001 = df_st_CH_2001.loc[:,"Total"]
s1_24 = np.sum(t_CH_2001)
s1_24
Out[58]:
In [59]:
df_st_CT_2001 = df_2001.loc[(df_2001["State"]=="CHHATTISGARH")]
df_st_CT_2001
Out[59]:
In [60]:
t_CT_2001 = df_st_CT_2001.loc[:,"Total"]
s1_25 = np.sum(t_CT_2001)
s1_25
Out[60]:
In [61]:
df_st_DN_2001 = df_2001.loc[(df_2001["State"]=="D & N HAVELI")]
df_st_DN_2001
Out[61]:
In [62]:
t_DN_2001 = df_st_DN_2001.loc[:,"Total"]
s1_26 = np.sum(t_DN_2001)
s1_26
Out[62]:
In [63]:
df_st_DD_2001 = df_2001.loc[(df_2001["State"]=="DAMAN & DIU")]
df_st_DD_2001
Out[63]:
In [64]:
t_DD_2001 = df_st_DD_2001.loc[:,"Total"]
s1_27 = np.sum(t_DD_2001)
s1_27
Out[64]:
In [65]:
df_st_DL_2001 = df_2001.loc[(df_2001["State"]=="DELHI (UT)")]
df_st_DL_2001
Out[65]:
In [66]:
t_DL_2001 = df_st_DL_2001.loc[:,"Total"]
s1_28 = np.sum(t_DL_2001)
s1_28
Out[66]:
In [67]:
df_st_GOA_2001 = df_2001.loc[(df_2001["State"]=="GOA")]
df_st_GOA_2001
Out[67]:
In [68]:
t_GOA_2001 = df_st_GOA_2001.loc[:,"Total"]
s1_29 = np.sum(t_GOA_2001)
s1_29
Out[68]:
In [69]:
df_st_GJ_2001 = df_2001.loc[(df_2001["State"]=="GUJARAT")]
df_st_GJ_2001
Out[69]:
In [70]:
t_GJ_2001 = df_st_GJ_2001.loc[:,"Total"]
s1_30 = np.sum(t_GJ_2001)
s1_30
Out[70]:
In [71]:
df_st_HR_2001 = df_2001.loc[(df_2001["State"]=="HARYANA")]
df_st_HR_2001
Out[71]:
In [72]:
t_HR_2001 = df_st_HR_2001.loc[:,"Total"]
s1_31 = np.sum(t_HR_2001)
s1_31
Out[72]:
In [73]:
df_st_HP_2001 = df_2001.loc[(df_2001["State"]=="HIMACHAL PRADESH")]
df_st_HP_2001
Out[73]:
In [74]:
t_HP_2001 = df_st_HP_2001.loc[:,"Total"]
s1_32 = np.sum(t_HP_2001)
s1_32
Out[74]:
In [75]:
df_st_JK_2001 = df_2001.loc[(df_2001["State"]=="JAMMU & KASHMIR")]
df_st_JK_2001
Out[75]:
In [76]:
t_JK_2001 = df_st_JK_2001.loc[:,"Total"]
s1_33 = np.sum(t_JK_2001)
s1_33
Out[76]:
In [77]:
df_st_JH_2001 = df_2001.loc[(df_2001["State"]=="JHARKHAND")]
df_st_JH_2001
Out[77]:
In [78]:
t_JH_2001 = df_st_JH_2001.loc[:,"Total"]
s1_34 = np.sum(t_JH_2001)
s1_34
Out[78]:
In [79]:
df_st_KN_2001 = df_2001.loc[(df_2001["State"]=="KARNATAKA")]
df_st_KN_2001
Out[79]:
In [80]:
t_KN_2001 = df_st_KN_2001.loc[:,"Total"]
s1_35 = np.sum(t_KN_2001)
s1_35
Out[80]:
In [81]:
df_st_KER_2001 = df_2001.loc[(df_2001["State"]=="KERALA")]
df_st_KER_2001
Out[81]:
In [82]:
t_KER_2001 = df_st_KER_2001.loc[:,"Total"]
s1_36 = np.sum(t_KER_2001)
s1_36
Out[82]:
In [83]:
df_st_LD_2001 = df_2001.loc[(df_2001["State"]=="LAKSHADWEEP")]
df_st_LD_2001
Out[83]:
In [84]:
t_LD_2001 = df_st_LD_2001.loc[:,"Total"]
s1_37 = np.sum(t_LD_2001)
s1_37
Out[84]:
In [85]:
df_st_MP_2001 = df_2001.loc[(df_2001["State"]=="MADHYA PRADESH")]
df_st_MP_2001
Out[85]:
In [86]:
t_MP_2001 = df_st_MP_2001.loc[:,"Total"]
s1_38 = np.sum(t_MP_2001)
s1_38
Out[86]:
In [87]:
df_st_MH_2001 = df_2001.loc[(df_2001["State"]=="MAHARASHTRA")]
df_st_MH_2001
Out[87]:
In [88]:
t_MH_2001 = df_st_MH_2001.loc[:,"Total"]
s1_39 = np.sum(t_MH_2001)
s1_39
Out[88]:
In [89]:
df_st_MN_2001 = df_2001.loc[(df_2001["State"]=="MANIPUR")]
df_st_MN_2001
Out[89]:
In [90]:
t_MN_2001 = df_st_MN_2001.loc[:,"Total"]
s1_40 = np.sum(t_MN_2001)
s1_40
Out[90]:
In [91]:
df_st_MG_2001 = df_2001.loc[(df_2001["State"]=="MEGHALAYA")]
df_st_MG_2001
Out[91]:
In [92]:
t_MG_2001 = df_st_MG_2001.loc[:,"Total"]
s1_41 = np.sum(t_MG_2001)
s1_41
Out[92]:
In [93]:
df_st_MZ_2001 = df_2001.loc[(df_2001["State"]=="MIZORAM")]
df_st_MZ_2001
Out[93]:
In [94]:
t_MN_2001 = df_st_MN_2001.loc[:,"Total"]
s1_42 = np.sum(t_MN_2001)
s1_42
Out[94]:
In [95]:
df_st_NG_2001 = df_2001.loc[(df_2001["State"]=="NAGALAND")]
df_st_NG_2001
Out[95]:
In [96]:
t_NG_2001 = df_st_NG_2001.loc[:,"Total"]
s1_43 = np.sum(t_NG_2001)
s1_43
Out[96]:
In [97]:
df_st_OD_2001 = df_2001.loc[(df_2001["State"]=="ODISHA")]
df_st_OD_2001
Out[97]:
In [98]:
t_OD_2001 = df_st_OD_2001.loc[:,"Total"]
s1_44 = np.sum(t_OD_2001)
s1_44
Out[98]:
In [99]:
df_st_PD_2001 = df_2001.loc[(df_2001["State"]=="PUDUCHERRY")]
df_st_PD_2001
Out[99]:
In [100]:
t_PD_2001 = df_st_PD_2001.loc[:,"Total"]
s1_45 = np.sum(t_PD_2001)
s1_45
Out[100]:
In [101]:
df_st_PB_2001 = df_2001.loc[(df_2001["State"]=="PUNJAB")]
df_st_PB_2001
Out[101]:
In [102]:
t_PB_2001 = df_st_PB_2001.loc[:,"Total"]
s1_46 = np.sum(t_PB_2001)
s1_46
Out[102]:
In [103]:
df_st_RJ_2001 = df_2001.loc[(df_2001["State"]=="RAJASTHAN")]
df_st_RJ_2001
Out[103]:
In [104]:
t_RJ_2001 = df_st_RJ_2001.loc[:,"Total"]
s1_47 = np.sum(t_RJ_2001)
s1_47
Out[104]:
In [105]:
df_st_SM_2001 = df_2001.loc[(df_2001["State"]=="SIKKIM")]
df_st_SM_2001
Out[105]:
In [106]:
t_SM_2001 = df_st_SM_2001.loc[:,"Total"]
s1_48 = np.sum(t_SM_2001)
s1_48
Out[106]:
In [107]:
df_st_TN_2001 = df_2001.loc[(df_2001["State"]=="TAMIL NADU")]
df_st_TN_2001
Out[107]:
In [108]:
t_TN_2001 = df_st_TN_2001.loc[:,"Total"]
s1_49 = np.sum(t_TN_2001)
s1_49
Out[108]:
In [109]:
df_st_TP_2001 = df_2001.loc[(df_2001["State"]=="TRIPURA")]
df_st_TP_2001
Out[109]:
In [110]:
t_TP_2001 = df_st_TP_2001.loc[:,"Total"]
s1_50 = np.sum(t_TP_2001)
s1_50
Out[110]:
In [111]:
df_st_UP_2001 = df_2001.loc[(df_2001["State"]=="UTTAR PRADESH")]
df_st_UP_2001
Out[111]:
In [112]:
t_UP_2001 = df_st_UP_2001.loc[:,"Total"]
s1_51 = np.sum(t_UP_2001)
s1_51
Out[112]:
In [113]:
df_st_UK_2001 = df_2001.loc[(df_2001["State"]=="UTTARAKHAND")]
df_st_UK_2001
Out[113]:
In [114]:
t_UK_2001 = df_st_UK_2001.loc[:,"Total"]
s1_52 = np.sum(t_UK_2001)
s1_52
Out[114]:
In [115]:
df_st_WB_2001 = df_2001.loc[(df_2001["State"]=="WEST BENGAL")]
df_st_WB_2001
Out[115]:
In [116]:
t_WB_2001 = df_st_WB_2001.loc[:,"Total"]
s1_53 = np.sum(t_WB_2001)
s1_53
Out[116]:
In [117]:
t_ANm_2001 = np.sum(df_st_AN_2001.where(df_st_AN_2001["Gender"]=="Male")["Total"])
t_ANm_2001
Out[117]:
In [118]:
t_ANfm_2001 = np.sum(df_st_AN_2001.where(df_st_AN_2001["Gender"]=="Female")["Total"])
t_ANfm_2001
Out[118]:
In [119]:
t_APm_2001 = np.sum(df_st_AP_2001.where(df_st_AP_2001["Gender"]=="Male")["Total"])
t_APm_2001
Out[119]:
In [120]:
t_APfm_2001 = np.sum(df_st_AP_2001.where(df_st_AP_2001["Gender"]=="Female")["Total"])
t_APfm_2001
Out[120]:
In [121]:
t_ARPm_2001 = np.sum(df_st_ARP_2001.where(df_st_ARP_2001["Gender"]=="Male")["Total"])
t_ARPm_2001
Out[121]:
In [122]:
t_ARPfm_2001 = np.sum(df_st_ARP_2001.where(df_st_ARP_2001["Gender"]=="Female")["Total"])
t_ARPfm_2001
Out[122]:
In [123]:
t_ASm_2001 = np.sum(df_st_AS_2001.where(df_st_AS_2001["Gender"]=="Male")["Total"])
t_ASm_2001
Out[123]:
In [124]:
t_ASfm_2001 = np.sum(df_st_AS_2001.where(df_st_AS_2001["Gender"]=="Female")["Total"])
t_ASfm_2001
Out[124]:
In [125]:
t_BHm_2001 = np.sum(df_st_BH_2001.where(df_st_BH_2001["Gender"]=="Male")["Total"])
t_BHm_2001
Out[125]:
In [126]:
t_BHfm_2001 = np.sum(df_st_BH_2001.where(df_st_BH_2001["Gender"]=="Female")["Total"])
t_BHfm_2001
Out[126]:
In [127]:
t_CHm_2001 = np.sum(df_st_CH_2001.where(df_st_CH_2001["Gender"]=="Male")["Total"])
t_CHm_2001
Out[127]:
In [128]:
t_CHfm_2001 = np.sum(df_st_CH_2001.where(df_st_CH_2001["Gender"]=="Female")["Total"])
t_CHfm_2001
Out[128]:
In [129]:
t_CTm_2001 = np.sum(df_st_CT_2001.where(df_st_CT_2001["Gender"]=="Male")["Total"])
t_CTm_2001
Out[129]:
In [130]:
t_CTfm_2001 = np.sum(df_st_CT_2001.where(df_st_CT_2001["Gender"]=="Female")["Total"])
t_CTfm_2001
Out[130]:
In [131]:
t_DNm_2001 = np.sum(df_st_DN_2001.where(df_st_DN_2001["Gender"]=="Male")["Total"])
t_DNm_2001
Out[131]:
In [132]:
t_DNfm_2001 = np.sum(df_st_DN_2001.where(df_st_DN_2001["Gender"]=="Female")["Total"])
t_DNfm_2001
Out[132]:
In [133]:
t_DDm_2001 = np.sum(df_st_DD_2001.where(df_st_DD_2001["Gender"]=="Male")["Total"])
t_DDm_2001
Out[133]:
In [134]:
t_DDfm_2001 = np.sum(df_st_DD_2001.where(df_st_DD_2001["Gender"]=="Female")["Total"])
t_DDfm_2001
Out[134]:
In [135]:
t_DLm_2001 = np.sum(df_st_DL_2001.where(df_st_DL_2001["Gender"]=="Male")["Total"])
t_DLm_2001
Out[135]:
In [136]:
t_DLfm_2001 = np.sum(df_st_DL_2001.where(df_st_DL_2001["Gender"]=="Female")["Total"])
t_DLfm_2001
Out[136]:
In [137]:
t_GOAm_2001 = np.sum(df_st_GOA_2001.where(df_st_GOA_2001["Gender"]=="Male")["Total"])
t_GOAm_2001
Out[137]:
In [138]:
t_GOAfm_2001 = np.sum(df_st_GOA_2001.where(df_st_GOA_2001["Gender"]=="Female")["Total"])
t_GOAfm_2001
Out[138]:
In [139]:
t_GJm_2001 = np.sum(df_st_GJ_2001.where(df_st_GJ_2001["Gender"]=="Male")["Total"])
t_GJm_2001
Out[139]:
In [140]:
t_GJfm_2001 = np.sum(df_st_GJ_2001.where(df_st_GJ_2001["Gender"]=="Female")["Total"])
t_GJfm_2001
Out[140]:
In [141]:
t_HRm_2001 = np.sum(df_st_HR_2001.where(df_st_HR_2001["Gender"]=="Male")["Total"])
t_HRm_2001
Out[141]:
In [142]:
t_HRfm_2001 = np.sum(df_st_HR_2001.where(df_st_HR_2001["Gender"]=="Female")["Total"])
t_HRfm_2001
Out[142]:
In [143]:
t_HPm_2001 = np.sum(df_st_HP_2001.where(df_st_HP_2001["Gender"]=="Male")["Total"])
t_HPm_2001
Out[143]:
In [144]:
t_HPfm_2001 = np.sum(df_st_HP_2001.where(df_st_HP_2001["Gender"]=="Female")["Total"])
t_HPfm_2001
Out[144]:
In [145]:
t_JKm_2001 = np.sum(df_st_JK_2001.where(df_st_JK_2001["Gender"]=="Male")["Total"])
t_JKm_2001
Out[145]:
In [146]:
t_JKfm_2001 = np.sum(df_st_JK_2001.where(df_st_JK_2001["Gender"]=="Female")["Total"])
t_JKfm_2001
Out[146]:
In [147]:
t_JHm_2001 = np.sum(df_st_JH_2001.where(df_st_JH_2001["Gender"]=="Male")["Total"])
t_JHm_2001
Out[147]:
In [148]:
t_JHfm_2001 = np.sum(df_st_JH_2001.where(df_st_JH_2001["Gender"]=="Female")["Total"])
t_JHfm_2001
Out[148]:
In [149]:
t_KNm_2001 = np.sum(df_st_KN_2001.where(df_st_KN_2001["Gender"]=="Male")["Total"])
t_KNm_2001
Out[149]:
In [150]:
t_KNfm_2001 = np.sum(df_st_KN_2001.where(df_st_KN_2001["Gender"]=="Female")["Total"])
t_KNfm_2001
Out[150]:
In [151]:
t_KERm_2001 = np.sum(df_st_KER_2001.where(df_st_KER_2001["Gender"]=="Male")["Total"])
t_KERm_2001
Out[151]:
In [152]:
t_KERfm_2001 = np.sum(df_st_KER_2001.where(df_st_KER_2001["Gender"]=="Female")["Total"])
t_KERfm_2001
Out[152]:
In [153]:
t_LDm_2001 = np.sum(df_st_LD_2001.where(df_st_LD_2001["Gender"]=="Male")["Total"])
t_LDm_2001
Out[153]:
In [154]:
t_LDfm_2001 = np.sum(df_st_LD_2001.where(df_st_LD_2001["Gender"]=="Female")["Total"])
t_LDfm_2001
Out[154]:
In [155]:
t_MPm_2001 = np.sum(df_st_MP_2001.where(df_st_MP_2001["Gender"]=="Male")["Total"])
t_MPm_2001
Out[155]:
In [156]:
t_MPfm_2001 = np.sum(df_st_MP_2001.where(df_st_MP_2001["Gender"]=="Female")["Total"])
t_MPfm_2001
Out[156]:
In [157]:
t_MHm_2001 = np.sum(df_st_MH_2001.where(df_st_MH_2001["Gender"]=="Male")["Total"])
t_MHm_2001
Out[157]:
In [158]:
t_MHfm_2001 = np.sum(df_st_MH_2001.where(df_st_MH_2001["Gender"]=="Female")["Total"])
t_MHfm_2001
Out[158]:
In [159]:
t_MNm_2001 = np.sum(df_st_MN_2001.where(df_st_MN_2001["Gender"]=="Male")["Total"])
t_MNm_2001
Out[159]:
In [160]:
t_MNfm_2001 = np.sum(df_st_MN_2001.where(df_st_MN_2001["Gender"]=="Female")["Total"])
t_MNfm_2001
Out[160]:
In [161]:
t_MGm_2001 = np.sum(df_st_MG_2001.where(df_st_MG_2001["Gender"]=="Male")["Total"])
t_MGm_2001
Out[161]:
In [162]:
t_MGfm_2001 = np.sum(df_st_MG_2001.where(df_st_MG_2001["Gender"]=="Female")["Total"])
t_MGfm_2001
Out[162]:
In [163]:
t_MZm_2001 = np.sum(df_st_MZ_2001.where(df_st_MZ_2001["Gender"]=="Male")["Total"])
t_MZm_2001
Out[163]:
In [164]:
t_MZfm_2001 = np.sum(df_st_MZ_2001.where(df_st_MZ_2001["Gender"]=="Female")["Total"])
t_MZfm_2001
Out[164]:
In [165]:
t_NGm_2001 = np.sum(df_st_NG_2001.where(df_st_NG_2001["Gender"]=="Male")["Total"])
t_NGm_2001
Out[165]:
In [166]:
t_NGfm_2001 = np.sum(df_st_NG_2001.where(df_st_NG_2001["Gender"]=="Female")["Total"])
t_MGfm_2001
Out[166]:
In [167]:
t_ODm_2001 = np.sum(df_st_OD_2001.where(df_st_OD_2001["Gender"]=="Male")["Total"])
t_ODm_2001
Out[167]:
In [168]:
t_ODfm_2001 = np.sum(df_st_OD_2001.where(df_st_OD_2001["Gender"]=="Female")["Total"])
t_ODfm_2001
Out[168]:
In [169]:
t_PDm_2001 = np.sum(df_st_PD_2001.where(df_st_PD_2001["Gender"]=="Male")["Total"])
t_PDm_2001
Out[169]:
In [170]:
t_PDfm_2001 = np.sum(df_st_PD_2001.where(df_st_PD_2001["Gender"]=="Female")["Total"])
t_PDfm_2001
Out[170]:
In [171]:
t_PBm_2001 = np.sum(df_st_PB_2001.where(df_st_PB_2001["Gender"]=="Male")["Total"])
t_PBm_2001
Out[171]:
In [172]:
t_PBfm_2001 = np.sum(df_st_PB_2001.where(df_st_PB_2001["Gender"]=="Female")["Total"])
t_PBfm_2001
Out[172]:
In [173]:
t_RJm_2001 = np.sum(df_st_RJ_2001.where(df_st_RJ_2001["Gender"]=="Male")["Total"])
t_RJm_2001
Out[173]:
In [174]:
t_RJfm_2001 = np.sum(df_st_RJ_2001.where(df_st_RJ_2001["Gender"]=="Female")["Total"])
t_RJfm_2001
Out[174]:
In [175]:
t_SMm_2001 = np.sum(df_st_SM_2001.where(df_st_SM_2001["Gender"]=="Male")["Total"])
t_SMm_2001
Out[175]:
In [176]:
t_SMfm_2001 = np.sum(df_st_SM_2001.where(df_st_SM_2001["Gender"]=="Female")["Total"])
t_SMfm_2001
Out[176]:
In [177]:
t_TNm_2001 = np.sum(df_st_TN_2001.where(df_st_TN_2001["Gender"]=="Male")["Total"])
t_TNm_2001
Out[177]:
In [178]:
t_TNfm_2001 = np.sum(df_st_TN_2001.where(df_st_TN_2001["Gender"]=="Female")["Total"])
t_TNfm_2001
Out[178]:
In [179]:
t_TPm_2001 = np.sum(df_st_TP_2001.where(df_st_TP_2001["Gender"]=="Male")["Total"])
t_TPm_2001
Out[179]:
In [180]:
t_TPfm_2001 = np.sum(df_st_TP_2001.where(df_st_TP_2001["Gender"]=="Female")["Total"])
t_TPfm_2001
Out[180]:
In [181]:
t_UPm_2001 = np.sum(df_st_UP_2001.where(df_st_UP_2001["Gender"]=="Male")["Total"])
t_UPm_2001
Out[181]:
In [182]:
t_UPfm_2001 = np.sum(df_st_UP_2001.where(df_st_UP_2001["Gender"]=="Female")["Total"])
t_UPfm_2001
Out[182]:
In [183]:
t_UKm_2001 = np.sum(df_st_UK_2001.where(df_st_UK_2001["Gender"]=="Male")["Total"])
t_UKm_2001
Out[183]:
In [184]:
t_UKfm_2001 = np.sum(df_st_UK_2001.where(df_st_UK_2001["Gender"]=="Female")["Total"])
t_UKfm_2001
Out[184]:
In [185]:
t_WBm_2001 = np.sum(df_st_WB_2001.where(df_st_WB_2001["Gender"]=="Male")["Total"])
t_WBm_2001
Out[185]:
In [186]:
t_WBfm_2001 = np.sum(df_st_WB_2001.where(df_st_WB_2001["Gender"]=="Female")["Total"])
t_WBfm_2001
Out[186]:
In [187]:
l_2001=list(df_2001.loc[:,"Type"].unique())
l_2001.sort()
print(l_2001,end=" ")
In [188]:
cause_l_2001=[]
for i in l_2001:
rough= np.sum(df_2001.where(df_2001["Type"]==i)["Total"])
cause_l_2001.append(rough)
print(cause_l_2001,end=" ")
In [189]:
x1_2001 = set(df_2001.loc[:,"State"])
x1_2001
len(x1_2001)
Out[189]:
In [190]:
cause_df_2001 = pd.DataFrame(cause_l_2001,l_2001)
cause_df_2001.reset_index()
Out[190]:
In [191]:
cause_df_2001.plot.bar(stacked=True,figsize=(50,20))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("Causes",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths due to Different Causes in 2001",fontsize=55)
plt.legend(["Death Count"], prop={"size":30})
Out[191]:
In [192]:
s_2001=list(df_2001.loc[:,"State"].unique())
s_2001.sort()
print(s_2001,end=" ")
In [193]:
state_s_2001 = [s1_19,s1_20,s1_21,s1_22,s1_23,s1_24,s1_25,s1_26,s1_27,s1_28,s1_29,s1_30,s1_31,s1_32,s1_33,s1_34,
s1_35,s1_36,s1_37,s1_38,s1_39,s1_40,s1_41,s1_42,s1_43,s1_44,s1_45,s1_46,s1_47,s1_48,s1_49,s1_50,
s1_51,s1_52,s1_53]
print(state_s_2001,end=" ")
In [194]:
state_m_2001=[t_ANm_2001, t_APm_2001, t_ARPm_2001, t_ASm_2001, t_BHm_2001, t_CHm_2001, t_CTm_2001, t_DNm_2001,
t_DDm_2001, t_DLm_2001, t_GOAm_2001, t_GJm_2001, t_HRm_2001, t_HPm_2001,t_JKm_2001, t_JHm_2001,
t_KNm_2001, t_KERm_2001, t_LDm_2001,t_MPm_2001,t_MHm_2001,t_MNm_2001,t_MGm_2001,t_MZm_2001,
t_NGm_2001, t_ODm_2001, t_PDm_2001,t_PBm_2001,t_RJm_2001,t_SMm_2001,t_TNm_2001,t_TPm_2001,
t_UPm_2001, t_UKm_2001, t_WBm_2001]
print(state_m_2001,end=" ")
In [195]:
state_fm_2001=[t_ANfm_2001, t_APfm_2001, t_ARPfm_2001, t_ASfm_2001, t_BHfm_2001, t_CHfm_2001, t_CTfm_2001, t_DNfm_2001,
t_DDfm_2001, t_DLfm_2001, t_GOAfm_2001, t_GJfm_2001, t_HRfm_2001, t_HPfm_2001,t_JKfm_2001, t_JHfm_2001,
t_KNfm_2001, t_KERfm_2001, t_LDfm_2001,t_MPfm_2001,t_MHfm_2001,t_MNfm_2001,t_MGfm_2001,t_MZfm_2001,
t_NGfm_2001, t_ODfm_2001, t_PDfm_2001,t_PBfm_2001,t_RJfm_2001,t_SMfm_2001,t_TNfm_2001,t_TPfm_2001,
t_UPfm_2001, t_UKfm_2001, t_WBfm_2001]
print(state_fm_2001,end=" ")
In [196]:
df_state_2001 = {
"State":['A & N ISLANDS', 'ANDHRA PRADESH', 'ARUNACHAL PRADESH', 'ASSAM', 'BIHAR', 'CHANDIGARH', 'CHHATTISGARH',
'D & N HAVELI', 'DAMAN & DIU', 'DELHI (UT)', 'GOA', 'GUJARAT', 'HARYANA', 'HIMACHAL PRADESH', 'JAMMU & KASHMIR',
'JHARKHAND', 'KARNATAKA', 'KERALA', 'LAKSHADWEEP', 'MADHYA PRADESH', 'MAHARASHTRA', 'MANIPUR', 'MEGHALAYA',
'MIZORAM', 'NAGALAND', 'ODISHA', 'PUDUCHERRY', 'PUNJAB', 'RAJASTHAN', 'SIKKIM', 'TAMIL NADU', 'TRIPURA',
'UTTAR PRADESH', 'UTTARAKHAND', 'WEST BENGAL'],
"Total":[s1_19,s1_20,s1_21,s1_22,s1_23,s1_24,s1_25,s1_26,s1_27,s1_28,s1_29,s1_30,s1_31,s1_32,s1_33,s1_34,
s1_35,s1_36,s1_37,s1_38,s1_39,s1_40,s1_41,s1_42,s1_43,s1_44,s1_45,s1_46,s1_47,s1_48,s1_49,s1_50,
s1_51,s1_52,s1_53],
"Male":[t_ANm_2001, t_APm_2001, t_ARPm_2001, t_ASm_2001, t_BHm_2001, t_CHm_2001, t_CTm_2001, t_DNm_2001,
t_DDm_2001, t_DLm_2001, t_GOAm_2001, t_GJm_2001, t_HRm_2001, t_HPm_2001,t_JKm_2001, t_JHm_2001,
t_KNm_2001, t_KERm_2001, t_LDm_2001,t_MPm_2001,t_MHm_2001,t_MNm_2001,t_MGm_2001,t_MZm_2001,
t_NGm_2001, t_ODm_2001, t_PDm_2001,t_PBm_2001,t_RJm_2001,t_SMm_2001,t_TNm_2001,t_TPm_2001,
t_UPm_2001, t_UKm_2001, t_WBm_2001],
"Female":[t_ANfm_2001, t_APfm_2001, t_ARPfm_2001, t_ASfm_2001, t_BHfm_2001, t_CHfm_2001, t_CTfm_2001, t_DNfm_2001,
t_DDfm_2001, t_DLfm_2001, t_GOAfm_2001, t_GJfm_2001, t_HRfm_2001, t_HPfm_2001,t_JKfm_2001, t_JHfm_2001,
t_KNfm_2001, t_KERfm_2001, t_LDfm_2001,t_MPfm_2001,t_MHfm_2001,t_MNfm_2001,t_MGfm_2001,t_MZfm_2001,
t_NGfm_2001, t_ODfm_2001, t_PDfm_2001,t_PBfm_2001,t_RJfm_2001,t_SMfm_2001,t_TNfm_2001,t_TPfm_2001,
t_UPfm_2001, t_UKfm_2001, t_WBfm_2001]
}
df_All_st_2001 = pd.DataFrame(df_state_2001)
df_All_st_2001
Out[196]:
In [197]:
pivot_2001 = pd.pivot_table(df_All_st_2001, values=None, index='State', columns=None,
fill_value=None, margins=False, dropna=True, margins_name='All', observed=False)
pivot_2001
Out[197]:
In [198]:
pivot_2001.plot(kind='bar',figsize=(60,30))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("States",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths of Males And Females In Different State",fontsize=55)
plt.legend(["Female","Male","Total"], prop={"size":30})
plt.show
Out[198]:
In [199]:
cause_l_percent_2001=[]
for i in cause_l_2001:
p=i/np.sum(cause_l_2001)
avg_2001_cause=p*100
cause_l_percent_2001.append(avg_2001_cause)
print(cause_l_percent_2001,end=" ")
In [200]:
np.max(cause_l_percent_2001) #Married
Out[200]:
In [201]:
np.min(cause_l_percent_2001) #Ideological Causes/Hero Worshipping
Out[201]:
In [202]:
state_s_2001_percent=[]
tot=np.sum(state_s_2001)
for i in state_s_2001:
xy=i/tot
avg_state_2001=xy*100
state_s_2001_percent.append(avg_state_2001)
print(state_s_2001_percent,end=" ")
In [203]:
np.max(state_s_2001_percent) #Maharashtra
Out[203]:
In [204]:
np.min(state_s_2001_percent) #Lakshdweep
Out[204]:
In [205]:
l_age_grp_2001=[s1_4,s1_5,s1_6,s1_7,s1_8,s1_54]
l_age_grp_2001
Out[205]:
In [206]:
l_age_grp_2001_percent=[]
for i in l_age_grp_2001:
yz=i/s1_1
avg_2001_age_grp=yz*100
l_age_grp_2001_percent.append(avg_2001_age_grp)
l_age_grp_2001_percent
Out[206]:
In [207]:
np.max(l_age_grp_2001_percent) #0-100+
Out[207]:
In [208]:
np.min(l_age_grp_2001_percent) #0-14
Out[208]:
In [209]:
total_male_percent_2001= (s1_2/s1_1)*100
total_male_percent_2001
Out[209]:
In [210]:
total_female_percent_2001= (s1_3/s1_1)*100
total_female_percent_2001
Out[210]:
In [211]:
l_mage_2001 = [s1_9,s1_11,s1_13,s1_15,s1_17,s1_56] # male age group-wise deathcount
l_fmage_2001 = [s1_10,s1_12,s1_14,s1_16,s1_18,s1_55] # female age group-wise deathcount
In [212]:
l_mage_2001
Out[212]:
In [213]:
l_fmage_2001
Out[213]:
In [214]:
age_m_pnt_2001 = [] #percentage of death count of each male age group
for i in l_mage_2001:
r1_2001= i/s1_2
r1_2001=r1_2001*100
age_m_pnt_2001.append(r1_2001)
age_m_pnt_2001 # 2nd highest - 30-44
Out[214]:
In [215]:
age_fm_pnt_2001 = [] #percentage of death count of each female age group
for i in l_fmage_2001:
r2_2001= i/s1_3
r2_2001=r2_2001*100
age_fm_pnt_2001.append(r2_2001)
age_fm_pnt_2001 # 2nd highest - 15-29
Out[215]:
2002¶
In [216]:
df_2002 = suicide_df.loc[(suicide_df["Year"]==2002)]
df_2002
Out[216]:
In [217]:
t2002_dcount = df_2002.loc[:,"Total"]
s2_1 = np.sum(t2002_dcount)
s2_1
Out[217]:
In [218]:
male_2002 = df_2002.loc[(df_2002["Gender"]=="Male")]
male_2002
Out[218]:
In [219]:
m2002_count = male_2002.loc[:,"Total"]
s2_2 = np.sum(m2002_count)
s2_2
Out[219]:
In [220]:
female_2002 = df_2002.loc[(df_2002["Gender"]=="Female")]
female_2002
Out[220]:
In [221]:
fm2002_count = female_2002.loc[:,"Total"]
s2_3 = np.sum(fm2002_count)
s2_3
Out[221]:
In [222]:
df_age_d014_2002 = df_2002.loc[(df_2002["Age_group"]=="0-14")]
df_age_d014_2002
Out[222]:
In [223]:
t_014_2002 = df_age_d014_2002.loc[:,"Total"]
s2_4 = np.sum(t_014_2002)
s2_4
Out[223]:
In [224]:
df_age_d1529_2002 = df_2002.loc[(df_2002["Age_group"]=="15-29")]
df_age_d1529_2002
Out[224]:
In [225]:
t_1529_2002 = df_age_d1529_2002.loc[:,"Total"]
s2_5 = np.sum(t_1529_2002)
s2_5
Out[225]:
In [226]:
df_age_d3044_2002 = df_2002.loc[(df_2002["Age_group"]=="30-44")]
df_age_d3044_2002
Out[226]:
In [227]:
t_3044_2002 = df_age_d3044_2002.loc[:,"Total"]
s2_6 = np.sum(t_3044_2002)
s2_6
Out[227]:
In [228]:
df_age_d4559_2002 = df_2002.loc[(df_2002["Age_group"]=="45-59")]
df_age_d4559_2002
Out[228]:
In [229]:
t_4559_2002 = df_age_d4559_2002.loc[:,"Total"]
s2_7 = np.sum(t_4559_2002)
s2_7
Out[229]:
In [230]:
df_age_d60_2002 = df_2002.loc[(df_2002["Age_group"]=="60+")]
df_age_d60_2002
Out[230]:
In [231]:
t_60_2002 = df_age_d60_2002.loc[:,"Total"]
s2_8 = np.sum(t_60_2002)
s2_8
Out[231]:
In [232]:
df_age_d0100_2002 = df_2002.loc[(df_2002["Age_group"]=="0-100+")]
df_age_d0100_2002
Out[232]:
In [233]:
t_0100_2002 = df_age_d0100_2002.loc[:,"Total"]
s2_54 = np.sum(t_0100_2002)
s2_54
Out[233]:
In [234]:
df_014m_2002 = df_age_d014_2002.loc[(df_age_d014_2002["Gender"]=="Male")]
df_014m_2002
Out[234]:
In [235]:
t_014m_2002 = df_014m_2002.loc[:,"Total"]
s2_9 = np.sum(t_014m_2002)
s2_9
Out[235]:
In [236]:
df_014fm_2002 = df_age_d014_2002.loc[(df_age_d014_2002["Gender"]=="Female")]
df_014fm_2002
Out[236]:
In [237]:
t_014fm_2002 = df_014fm_2002.loc[:,"Total"]
s2_10 = np.sum(t_014fm_2002)
s2_10
Out[237]:
In [238]:
df_1529m_2002 = df_age_d1529_2002.loc[(df_age_d1529_2002["Gender"]=="Male")]
df_1529m_2002
Out[238]:
In [239]:
t_1529m_2002 = df_1529m_2002.loc[:,"Total"]
s2_11 = np.sum(t_1529m_2002)
s2_11
Out[239]:
In [240]:
df_1529fm_2002 = df_age_d1529_2002.loc[(df_age_d1529_2002["Gender"]=="Female")]
df_1529fm_2002
Out[240]:
In [241]:
t_1529fm_2002 = df_1529fm_2002.loc[:,"Total"]
s2_12 = np.sum(t_1529fm_2002)
s2_12
Out[241]:
In [242]:
df_3044m_2002 = df_age_d3044_2002.loc[(df_age_d3044_2002["Gender"]=="Male")]
df_3044m_2002
Out[242]:
In [243]:
t_3044m_2002 = df_3044m_2002.loc[:,"Total"]
s2_13 = np.sum(t_3044m_2002)
s2_13
Out[243]:
In [244]:
df_3044fm_2002 = df_age_d3044_2002.loc[(df_age_d3044_2002["Gender"]=="Female")]
df_3044fm_2002
Out[244]:
In [245]:
t_3044fm_2002 = df_3044fm_2002.loc[:,"Total"]
s2_14 = np.sum(t_3044fm_2002)
s2_14
Out[245]:
In [246]:
df_4559m_2002 = df_age_d4559_2002.loc[(df_age_d4559_2002["Gender"]=="Male")]
df_4559m_2002
Out[246]:
In [247]:
t_4559m_2002 = df_4559m_2002.loc[:,"Total"]
s2_15 = np.sum(t_4559m_2002)
s2_15
Out[247]:
In [248]:
df_4559fm_2002 = df_age_d4559_2002.loc[(df_age_d4559_2002["Gender"]=="Female")]
df_4559fm_2002
Out[248]:
In [249]:
t_4559fm_2002 = df_4559fm_2002.loc[:,"Total"]
s2_16 = np.sum(t_4559fm_2002)
s2_16
Out[249]:
In [250]:
df_60m_2002 = df_age_d60_2002.loc[(df_age_d60_2002["Gender"]=="Male")]
df_60m_2002
Out[250]:
In [251]:
t_60m_2002 = df_60m_2002.loc[:,"Total"]
s2_17 = np.sum(t_60m_2002)
s2_17
Out[251]:
In [252]:
df_60fm_2002 = df_age_d60_2002.loc[(df_age_d60_2002["Gender"]=="Female")]
df_60fm_2002
Out[252]:
In [253]:
t_60fm_2002 = df_60fm_2002.loc[:,"Total"]
s2_18 = np.sum(t_60fm_2002)
s2_18
Out[253]:
In [254]:
df_0100fm_2002 = df_age_d0100_2002.loc[(df_age_d0100_2002["Gender"]=="Female")]
df_0100fm_2002
Out[254]:
In [255]:
t_0100fm_2002 = df_0100fm_2002.loc[:,"Total"]
s2_55 = np.sum(t_0100fm_2002)
s2_55
Out[255]:
In [256]:
df_0100m_2002 = df_age_d0100_2002.loc[(df_age_d0100_2002["Gender"]=="Male")]
df_0100m_2002
Out[256]:
In [257]:
t_0100m_2002 = df_0100m_2002.loc[:,"Total"]
s2_56 = np.sum(t_0100m_2002)
s2_56
Out[257]:
In [258]:
df_st_AN_2002 = df_2002.loc[(df_2002["State"]=="A & N ISLANDS")]
df_st_AN_2002
Out[258]:
In [259]:
t_AN_2002 = df_st_AN_2002.loc[:,"Total"]
s2_19 = np.sum(t_AN_2002)
s2_19
Out[259]:
In [260]:
df_st_AP_2002 = df_2002.loc[(df_2002["State"]=="ANDHRA PRADESH")]
df_st_AP_2002
Out[260]:
In [261]:
t_AP_2002 = df_st_AP_2002.loc[:,"Total"]
s2_20 = np.sum(t_AP_2002)
s2_20
Out[261]:
In [262]:
df_st_ARP_2002 = df_2002.loc[(df_2002["State"]=="ARUNACHAL PRADESH")]
df_st_ARP_2002
Out[262]:
In [263]:
t_ARP_2002 = df_st_ARP_2002.loc[:,"Total"]
s2_21 = np.sum(t_ARP_2002)
s2_21
Out[263]:
In [264]:
df_st_AS_2002 = df_2002.loc[(df_2002["State"]=="ASSAM")]
df_st_AS_2002
Out[264]:
In [265]:
t_AS_2002 = df_st_AS_2002.loc[:,"Total"]
s2_22 = np.sum(t_AS_2002)
s2_22
Out[265]:
In [266]:
df_st_BH_2002 = df_2002.loc[(df_2002["State"]=="BIHAR")]
df_st_BH_2002
Out[266]:
In [267]:
t_BH_2002 = df_st_BH_2002.loc[:,"Total"]
s2_23 = np.sum(t_BH_2002)
s2_23
Out[267]:
In [268]:
df_st_CH_2002 = df_2002.loc[(df_2002["State"]=="CHANDIGARH")]
df_st_CH_2002
Out[268]:
In [269]:
t_CH_2002 = df_st_CH_2002.loc[:,"Total"]
s2_24 = np.sum(t_CH_2002)
s2_24
Out[269]:
In [270]:
df_st_CT_2002 = df_2002.loc[(df_2002["State"]=="CHHATTISGARH")]
df_st_CT_2002
Out[270]:
In [271]:
t_CT_2002 = df_st_CT_2002.loc[:,"Total"]
s2_25 = np.sum(t_CT_2002)
s2_25
Out[271]:
In [272]:
df_st_DN_2002 = df_2002.loc[(df_2002["State"]=="D & N HAVELI")]
df_st_DN_2002
Out[272]:
In [273]:
t_DN_2002 = df_st_DN_2002.loc[:,"Total"]
s2_26 = np.sum(t_DN_2002)
s2_26
Out[273]:
In [274]:
df_st_DD_2002 = df_2002.loc[(df_2002["State"]=="DAMAN & DIU")]
df_st_DD_2002
Out[274]:
In [275]:
t_DD_2002 = df_st_DD_2002.loc[:,"Total"]
s2_27 = np.sum(t_DD_2002)
s2_27
Out[275]:
In [276]:
df_st_DL_2002 = df_2002.loc[(df_2002["State"]=="DELHI (UT)")]
df_st_DL_2002
Out[276]:
In [277]:
t_DL_2002 = df_st_DL_2002.loc[:,"Total"]
s2_28 = np.sum(t_DL_2002)
s2_28
Out[277]:
In [278]:
df_st_GOA_2002 = df_2002.loc[(df_2002["State"]=="GOA")]
df_st_GOA_2002
Out[278]:
In [279]:
t_GOA_2002 = df_st_GOA_2002.loc[:,"Total"]
s2_29 = np.sum(t_GOA_2002)
s2_29
Out[279]:
In [280]:
df_st_GJ_2002 = df_2002.loc[(df_2002["State"]=="GUJARAT")]
df_st_GJ_2002
Out[280]:
In [281]:
t_GJ_2002 = df_st_GJ_2002.loc[:,"Total"]
s2_30 = np.sum(t_GJ_2002)
s2_30
Out[281]:
In [282]:
df_st_HR_2002 = df_2002.loc[(df_2002["State"]=="HARYANA")]
df_st_HR_2002
Out[282]:
In [283]:
t_HR_2002 = df_st_HR_2002.loc[:,"Total"]
s2_31 = np.sum(t_HR_2002)
s2_31
Out[283]:
In [284]:
df_st_HP_2002 = df_2002.loc[(df_2002["State"]=="HIMACHAL PRADESH")]
df_st_HP_2002
Out[284]:
In [285]:
t_HP_2002 = df_st_HP_2002.loc[:,"Total"]
s2_32 = np.sum(t_HP_2002)
s2_32
Out[285]:
In [286]:
df_st_JK_2002 = df_2002.loc[(df_2002["State"]=="JAMMU & KASHMIR")]
df_st_JK_2002
Out[286]:
In [287]:
t_JK_2002 = df_st_JK_2002.loc[:,"Total"]
s2_33 = np.sum(t_JK_2002)
s2_33
Out[287]:
In [288]:
df_st_JH_2002 = df_2002.loc[(df_2002["State"]=="JHARKHAND")]
df_st_JH_2002
Out[288]:
In [289]:
t_JH_2002 = df_st_JH_2002.loc[:,"Total"]
s2_34 = np.sum(t_JH_2002)
s2_34
Out[289]:
In [290]:
df_st_KN_2002 = df_2002.loc[(df_2002["State"]=="KARNATAKA")]
df_st_KN_2002
Out[290]:
In [291]:
t_KN_2002 = df_st_KN_2002.loc[:,"Total"]
s2_35 = np.sum(t_KN_2002)
s2_35
Out[291]:
In [292]:
df_st_KER_2002 = df_2002.loc[(df_2002["State"]=="KERALA")]
df_st_KER_2002
Out[292]:
In [293]:
t_KER_2002 = df_st_KER_2002.loc[:,"Total"]
s2_36 = np.sum(t_KER_2002)
s2_36
Out[293]:
In [294]:
df_st_LD_2002 = df_2002.loc[(df_2002["State"]=="LAKSHADWEEP")]
df_st_LD_2002
Out[294]:
In [295]:
t_LD_2002 = df_st_LD_2002.loc[:,"Total"]
s2_37 = np.sum(t_LD_2002)
s2_37
Out[295]:
In [296]:
df_st_MP_2002 = df_2002.loc[(df_2002["State"]=="MADHYA PRADESH")]
df_st_MP_2002
Out[296]:
In [297]:
t_MP_2002 = df_st_MP_2002.loc[:,"Total"]
s2_38 = np.sum(t_MP_2002)
s2_38
Out[297]:
In [298]:
df_st_MH_2002 = df_2002.loc[(df_2002["State"]=="MAHARASHTRA")]
df_st_MH_2002
Out[298]:
In [299]:
t_MH_2002 = df_st_MH_2002.loc[:,"Total"]
s2_39 = np.sum(t_MH_2002)
s2_39
Out[299]:
In [300]:
df_st_MN_2002 = df_2002.loc[(df_2002["State"]=="MANIPUR")]
df_st_MN_2002
Out[300]:
In [301]:
t_MN_2002 = df_st_MN_2002.loc[:,"Total"]
s2_40 = np.sum(t_MN_2002)
s2_40
Out[301]:
In [302]:
df_st_MG_2002 = df_2002.loc[(df_2002["State"]=="MEGHALAYA")]
df_st_MG_2002
Out[302]:
In [303]:
t_MG_2002 = df_st_MG_2002.loc[:,"Total"]
s2_41 = np.sum(t_MG_2002)
s2_41
Out[303]:
In [304]:
df_st_MZ_2002 = df_2002.loc[(df_2002["State"]=="MIZORAM")]
df_st_MZ_2002
Out[304]:
In [305]:
t_MN_2002 = df_st_MN_2002.loc[:,"Total"]
s2_42 = np.sum(t_MN_2002)
s2_42
Out[305]:
In [306]:
df_st_NG_2002 = df_2002.loc[(df_2002["State"]=="NAGALAND")]
df_st_NG_2002
Out[306]:
In [307]:
t_NG_2002 = df_st_NG_2002.loc[:,"Total"]
s2_43 = np.sum(t_NG_2002)
s2_43
Out[307]:
In [308]:
df_st_OD_2002 = df_2002.loc[(df_2002["State"]=="ODISHA")]
df_st_OD_2002
Out[308]:
In [309]:
t_OD_2002 = df_st_OD_2002.loc[:,"Total"]
s2_44 = np.sum(t_OD_2002)
s2_44
Out[309]:
In [310]:
df_st_PD_2002 = df_2002.loc[(df_2002["State"]=="PUDUCHERRY")]
df_st_PD_2002
Out[310]:
In [311]:
t_PD_2002 = df_st_PD_2002.loc[:,"Total"]
s2_45 = np.sum(t_PD_2002)
s2_45
Out[311]:
In [312]:
df_st_PB_2002 = df_2002.loc[(df_2002["State"]=="PUNJAB")]
df_st_PB_2002
Out[312]:
In [313]:
t_PB_2002 = df_st_PB_2002.loc[:,"Total"]
s2_46 = np.sum(t_PB_2002)
s2_46
Out[313]:
In [314]:
df_st_RJ_2002 = df_2002.loc[(df_2002["State"]=="RAJASTHAN")]
df_st_RJ_2002
Out[314]:
In [315]:
t_RJ_2002 = df_st_RJ_2002.loc[:,"Total"]
s2_47 = np.sum(t_RJ_2002)
s2_47
Out[315]:
In [316]:
df_st_SM_2002 = df_2002.loc[(df_2002["State"]=="SIKKIM")]
df_st_SM_2002
Out[316]:
In [317]:
t_SM_2002 = df_st_SM_2002.loc[:,"Total"]
s2_48 = np.sum(t_SM_2002)
s2_48
Out[317]:
In [318]:
df_st_TN_2002 = df_2002.loc[(df_2002["State"]=="TAMIL NADU")]
df_st_TN_2002
Out[318]:
In [319]:
t_TN_2002 = df_st_TN_2002.loc[:,"Total"]
s2_49 = np.sum(t_TN_2002)
s2_49
Out[319]:
In [320]:
df_st_TP_2002 = df_2002.loc[(df_2002["State"]=="TRIPURA")]
df_st_TP_2002
Out[320]:
In [321]:
t_TP_2002 = df_st_TP_2002.loc[:,"Total"]
s2_50 = np.sum(t_TP_2002)
s2_50
Out[321]:
In [322]:
df_st_UP_2002 = df_2002.loc[(df_2002["State"]=="UTTAR PRADESH")]
df_st_UP_2002
Out[322]:
In [323]:
t_UP_2002 = df_st_UP_2002.loc[:,"Total"]
s2_51 = np.sum(t_UP_2002)
s2_51
Out[323]:
In [324]:
df_st_UK_2002 = df_2002.loc[(df_2002["State"]=="UTTARAKHAND")]
df_st_UK_2002
Out[324]:
In [325]:
t_UK_2002 = df_st_UK_2002.loc[:,"Total"]
s2_52 = np.sum(t_UK_2002)
s2_52
Out[325]:
In [326]:
df_st_WB_2002 = df_2002.loc[(df_2002["State"]=="WEST BENGAL")]
df_st_WB_2002
Out[326]:
In [327]:
t_WB_2002 = df_st_WB_2002.loc[:,"Total"]
s2_53 = np.sum(t_WB_2002)
s2_53
Out[327]:
In [328]:
t_ANm_2002 = np.sum(df_st_AN_2002.where(df_st_AN_2002["Gender"]=="Male")["Total"])
t_ANm_2002
Out[328]:
In [329]:
t_ANfm_2002 = np.sum(df_st_AN_2002.where(df_st_AN_2002["Gender"]=="Female")["Total"])
t_ANfm_2002
Out[329]:
In [330]:
t_APm_2002 = np.sum(df_st_AP_2002.where(df_st_AP_2002["Gender"]=="Male")["Total"])
t_APm_2002
Out[330]:
In [331]:
t_APfm_2002 = np.sum(df_st_AP_2002.where(df_st_AP_2002["Gender"]=="Female")["Total"])
t_APfm_2002
Out[331]:
In [332]:
t_ARPm_2002 = np.sum(df_st_ARP_2002.where(df_st_ARP_2002["Gender"]=="Male")["Total"])
t_ARPm_2002
Out[332]:
In [333]:
t_ARPfm_2002 = np.sum(df_st_ARP_2002.where(df_st_ARP_2002["Gender"]=="Female")["Total"])
t_ARPfm_2002
Out[333]:
In [334]:
t_ASm_2002 = np.sum(df_st_AS_2002.where(df_st_AS_2002["Gender"]=="Male")["Total"])
t_ASm_2002
Out[334]:
In [335]:
t_ASfm_2002 = np.sum(df_st_AS_2002.where(df_st_AS_2002["Gender"]=="Female")["Total"])
t_ASfm_2002
Out[335]:
In [336]:
t_BHm_2002 = np.sum(df_st_BH_2002.where(df_st_BH_2002["Gender"]=="Male")["Total"])
t_BHm_2002
Out[336]:
In [337]:
t_BHfm_2002 = np.sum(df_st_BH_2002.where(df_st_BH_2002["Gender"]=="Female")["Total"])
t_BHfm_2002
Out[337]:
In [338]:
t_CHm_2002 = np.sum(df_st_CH_2002.where(df_st_CH_2002["Gender"]=="Male")["Total"])
t_CHm_2002
Out[338]:
In [339]:
t_CHfm_2002 = np.sum(df_st_CH_2002.where(df_st_CH_2002["Gender"]=="Female")["Total"])
t_CHfm_2002
Out[339]:
In [340]:
t_CTm_2002 = np.sum(df_st_CT_2002.where(df_st_CT_2002["Gender"]=="Male")["Total"])
t_CTm_2002
Out[340]:
In [341]:
t_CTfm_2002 = np.sum(df_st_CT_2002.where(df_st_CT_2002["Gender"]=="Female")["Total"])
t_CTfm_2002
Out[341]:
In [342]:
t_DNm_2002 = np.sum(df_st_DN_2002.where(df_st_DN_2002["Gender"]=="Male")["Total"])
t_DNm_2002
Out[342]:
In [343]:
t_DNfm_2002 = np.sum(df_st_DN_2002.where(df_st_DN_2002["Gender"]=="Female")["Total"])
t_DNfm_2002
Out[343]:
In [344]:
t_DDm_2002 = np.sum(df_st_DD_2002.where(df_st_DD_2002["Gender"]=="Male")["Total"])
t_DDm_2002
Out[344]:
In [345]:
t_DDfm_2002 = np.sum(df_st_DD_2002.where(df_st_DD_2002["Gender"]=="Female")["Total"])
t_DDfm_2002
Out[345]:
In [346]:
t_DLm_2002 = np.sum(df_st_DL_2002.where(df_st_DL_2002["Gender"]=="Male")["Total"])
t_DLm_2002
Out[346]:
In [347]:
t_DLfm_2002 = np.sum(df_st_DL_2002.where(df_st_DL_2002["Gender"]=="Female")["Total"])
t_DLfm_2002
Out[347]:
In [348]:
t_GOAm_2002 = np.sum(df_st_GOA_2002.where(df_st_GOA_2002["Gender"]=="Male")["Total"])
t_GOAm_2002
Out[348]:
In [349]:
t_GOAfm_2002 = np.sum(df_st_GOA_2002.where(df_st_GOA_2002["Gender"]=="Female")["Total"])
t_GOAfm_2002
Out[349]:
In [350]:
t_GJm_2002 = np.sum(df_st_GJ_2002.where(df_st_GJ_2002["Gender"]=="Male")["Total"])
t_GJm_2002
Out[350]:
In [351]:
t_GJfm_2002 = np.sum(df_st_GJ_2002.where(df_st_GJ_2002["Gender"]=="Female")["Total"])
t_GJfm_2002
Out[351]:
In [352]:
t_HRm_2002 = np.sum(df_st_HR_2002.where(df_st_HR_2002["Gender"]=="Male")["Total"])
t_HRm_2002
Out[352]:
In [353]:
t_HRfm_2002 = np.sum(df_st_HR_2002.where(df_st_HR_2002["Gender"]=="Female")["Total"])
t_HRfm_2002
Out[353]:
In [354]:
t_HPm_2002 = np.sum(df_st_HP_2002.where(df_st_HP_2002["Gender"]=="Male")["Total"])
t_HPm_2002
Out[354]:
In [355]:
t_HPfm_2002 = np.sum(df_st_HP_2002.where(df_st_HP_2002["Gender"]=="Female")["Total"])
t_HPfm_2002
Out[355]:
In [356]:
t_JKm_2002 = np.sum(df_st_JK_2002.where(df_st_JK_2002["Gender"]=="Male")["Total"])
t_JKm_2002
Out[356]:
In [357]:
t_JKfm_2002 = np.sum(df_st_JK_2002.where(df_st_JK_2002["Gender"]=="Female")["Total"])
t_JKfm_2002
Out[357]:
In [358]:
t_JHm_2002 = np.sum(df_st_JH_2002.where(df_st_JH_2002["Gender"]=="Male")["Total"])
t_JHm_2002
Out[358]:
In [359]:
t_JHfm_2002 = np.sum(df_st_JH_2002.where(df_st_JH_2002["Gender"]=="Female")["Total"])
t_JHfm_2002
Out[359]:
In [360]:
t_KNm_2002 = np.sum(df_st_KN_2002.where(df_st_KN_2002["Gender"]=="Male")["Total"])
t_KNm_2002
Out[360]:
In [361]:
t_KNfm_2002 = np.sum(df_st_KN_2002.where(df_st_KN_2002["Gender"]=="Female")["Total"])
t_KNfm_2002
Out[361]:
In [362]:
t_KERm_2002 = np.sum(df_st_KER_2002.where(df_st_KER_2002["Gender"]=="Male")["Total"])
t_KERm_2002
Out[362]:
In [363]:
t_KERfm_2002 = np.sum(df_st_KER_2002.where(df_st_KER_2002["Gender"]=="Female")["Total"])
t_KERfm_2002
Out[363]:
In [364]:
t_LDm_2002 = np.sum(df_st_LD_2002.where(df_st_LD_2002["Gender"]=="Male")["Total"])
t_LDm_2002
Out[364]:
In [365]:
t_LDfm_2002 = np.sum(df_st_LD_2002.where(df_st_LD_2002["Gender"]=="Female")["Total"])
t_LDfm_2002
Out[365]:
In [366]:
t_MPm_2002 = np.sum(df_st_MP_2002.where(df_st_MP_2002["Gender"]=="Male")["Total"])
t_MPm_2002
Out[366]:
In [367]:
t_MPfm_2002 = np.sum(df_st_MP_2002.where(df_st_MP_2002["Gender"]=="Female")["Total"])
t_MPfm_2002
Out[367]:
In [368]:
t_MHm_2002 = np.sum(df_st_MH_2002.where(df_st_MH_2002["Gender"]=="Male")["Total"])
t_MHm_2002
Out[368]:
In [369]:
t_MHfm_2002 = np.sum(df_st_MH_2002.where(df_st_MH_2002["Gender"]=="Female")["Total"])
t_MHfm_2002
Out[369]:
In [370]:
t_MNm_2002 = np.sum(df_st_MN_2002.where(df_st_MN_2002["Gender"]=="Male")["Total"])
t_MNm_2002
Out[370]:
In [371]:
t_MNfm_2002 = np.sum(df_st_MN_2002.where(df_st_MN_2002["Gender"]=="Female")["Total"])
t_MNfm_2002
Out[371]:
In [372]:
t_MGm_2002 = np.sum(df_st_MG_2002.where(df_st_MG_2002["Gender"]=="Male")["Total"])
t_MGm_2002
Out[372]:
In [373]:
t_MGfm_2002 = np.sum(df_st_MG_2002.where(df_st_MG_2002["Gender"]=="Female")["Total"])
t_MGfm_2002
Out[373]:
In [374]:
t_MZm_2002 = np.sum(df_st_MZ_2002.where(df_st_MZ_2002["Gender"]=="Male")["Total"])
t_MZm_2002
Out[374]:
In [375]:
t_MZfm_2002 = np.sum(df_st_MZ_2002.where(df_st_MZ_2002["Gender"]=="Female")["Total"])
t_MZfm_2002
Out[375]:
In [376]:
t_NGm_2002 = np.sum(df_st_NG_2002.where(df_st_NG_2002["Gender"]=="Male")["Total"])
t_NGm_2002
Out[376]:
In [377]:
t_NGfm_2002 = np.sum(df_st_NG_2002.where(df_st_NG_2002["Gender"]=="Female")["Total"])
t_MGfm_2002
Out[377]:
In [378]:
t_ODm_2002 = np.sum(df_st_OD_2002.where(df_st_OD_2002["Gender"]=="Male")["Total"])
t_ODm_2002
Out[378]:
In [379]:
t_ODfm_2002 = np.sum(df_st_OD_2002.where(df_st_OD_2002["Gender"]=="Female")["Total"])
t_ODfm_2002
Out[379]:
In [380]:
t_PDm_2002 = np.sum(df_st_PD_2002.where(df_st_PD_2002["Gender"]=="Male")["Total"])
t_PDm_2002
Out[380]:
In [381]:
t_PDfm_2002 = np.sum(df_st_PD_2002.where(df_st_PD_2002["Gender"]=="Female")["Total"])
t_PDfm_2002
Out[381]:
In [382]:
t_PBm_2002 = np.sum(df_st_PB_2002.where(df_st_PB_2002["Gender"]=="Male")["Total"])
t_PBm_2002
Out[382]:
In [383]:
t_PBfm_2002 = np.sum(df_st_PB_2002.where(df_st_PB_2002["Gender"]=="Female")["Total"])
t_PBfm_2002
Out[383]:
In [384]:
t_RJm_2002 = np.sum(df_st_RJ_2002.where(df_st_RJ_2002["Gender"]=="Male")["Total"])
t_RJm_2002
Out[384]:
In [385]:
t_RJfm_2002 = np.sum(df_st_RJ_2002.where(df_st_RJ_2002["Gender"]=="Female")["Total"])
t_RJfm_2002
Out[385]:
In [386]:
t_SMm_2002 = np.sum(df_st_SM_2002.where(df_st_SM_2002["Gender"]=="Male")["Total"])
t_SMm_2002
Out[386]:
In [387]:
t_SMfm_2002 = np.sum(df_st_SM_2002.where(df_st_SM_2002["Gender"]=="Female")["Total"])
t_SMfm_2002
Out[387]:
In [388]:
t_TNm_2002 = np.sum(df_st_TN_2002.where(df_st_TN_2002["Gender"]=="Male")["Total"])
t_TNm_2002
Out[388]:
In [389]:
t_TNfm_2002 = np.sum(df_st_TN_2002.where(df_st_TN_2002["Gender"]=="Female")["Total"])
t_TNfm_2002
Out[389]:
In [390]:
t_TPm_2002 = np.sum(df_st_TP_2002.where(df_st_TP_2002["Gender"]=="Male")["Total"])
t_TPm_2002
Out[390]:
In [391]:
t_TPfm_2002 = np.sum(df_st_TP_2002.where(df_st_TP_2002["Gender"]=="Female")["Total"])
t_TPfm_2002
Out[391]:
In [392]:
t_UPm_2002 = np.sum(df_st_UP_2002.where(df_st_UP_2002["Gender"]=="Male")["Total"])
t_UPm_2002
Out[392]:
In [393]:
t_UPfm_2002 = np.sum(df_st_UP_2002.where(df_st_UP_2002["Gender"]=="Female")["Total"])
t_UPfm_2002
Out[393]:
In [394]:
t_UKm_2002 = np.sum(df_st_UK_2002.where(df_st_UK_2002["Gender"]=="Male")["Total"])
t_UKm_2002
Out[394]:
In [395]:
t_UKfm_2002 = np.sum(df_st_UK_2002.where(df_st_UK_2002["Gender"]=="Female")["Total"])
t_UKfm_2002
Out[395]:
In [396]:
t_WBm_2002 = np.sum(df_st_WB_2002.where(df_st_WB_2002["Gender"]=="Male")["Total"])
t_WBm_2002
Out[396]:
In [397]:
t_WBfm_2002 = np.sum(df_st_WB_2002.where(df_st_WB_2002["Gender"]=="Female")["Total"])
t_WBfm_2002
Out[397]:
In [398]:
l_2002=list(df_2002.loc[:,"Type"].unique())
l_2002.sort()
print(l_2002,end=" ")
In [399]:
cause_l_2002=[]
for i in l_2002:
rough= np.sum(df_2002.where(df_2002["Type"]==i)["Total"])
cause_l_2002.append(rough)
print(cause_l_2002,end=" ")
In [400]:
x1_2002 = set(df_2002.loc[:,"State"])
x1_2002
len(x1_2002)
Out[400]:
In [401]:
cause_df_2002 = pd.DataFrame(cause_l_2002,l_2002)
cause_df_2002.reset_index()
Out[401]:
In [402]:
cause_df_2002.plot.bar(stacked=True,figsize=(50,20))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("Causes",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths due to Different Causes in 2002",fontsize=55)
plt.legend(["Death Count"], prop={"size":30})
Out[402]:
In [403]:
s_2002=list(df_2002.loc[:,"State"].unique())
s_2002.sort()
print(s_2002,end=" ")
In [404]:
state_s_2002 = [s2_19,s2_20,s2_21,s2_22,s2_23,s2_24,s2_25,s2_26,s2_27,s2_28,s2_29,s2_30,s2_31,s2_32,s2_33,s2_34,
s2_35,s2_36,s2_37,s2_38,s2_39,s2_40,s2_41,s2_42,s2_43,s2_44,s2_45,s2_46,s2_47,s2_48,s2_49,s2_50,
s2_51,s2_52,s2_53]
print(state_s_2002,end=" ")
In [405]:
state_m_2002=[t_ANm_2002, t_APm_2002, t_ARPm_2002, t_ASm_2002, t_BHm_2002, t_CHm_2002, t_CTm_2002, t_DNm_2002,
t_DDm_2002, t_DLm_2002, t_GOAm_2002, t_GJm_2002, t_HRm_2002, t_HPm_2002,t_JKm_2002, t_JHm_2002,
t_KNm_2002, t_KERm_2002, t_LDm_2002,t_MPm_2002,t_MHm_2002,t_MNm_2002,t_MGm_2002,t_MZm_2002,
t_NGm_2002, t_ODm_2002, t_PDm_2002,t_PBm_2002,t_RJm_2002,t_SMm_2002,t_TNm_2002,t_TPm_2002,
t_UPm_2002, t_UKm_2002, t_WBm_2002]
print(state_m_2002,end=" ")
In [406]:
state_fm_2002=[t_ANfm_2002, t_APfm_2002, t_ARPfm_2002, t_ASfm_2002, t_BHfm_2002, t_CHfm_2002, t_CTfm_2002, t_DNfm_2002,
t_DDfm_2002, t_DLfm_2002, t_GOAfm_2002, t_GJfm_2002, t_HRfm_2002, t_HPfm_2002,t_JKfm_2002, t_JHfm_2002,
t_KNfm_2002, t_KERfm_2002, t_LDfm_2002,t_MPfm_2002,t_MHfm_2002,t_MNfm_2002,t_MGfm_2002,t_MZfm_2002,
t_NGfm_2002, t_ODfm_2002, t_PDfm_2002,t_PBfm_2002,t_RJfm_2002,t_SMfm_2002,t_TNfm_2002,t_TPfm_2002,
t_UPfm_2002, t_UKfm_2002, t_WBfm_2002]
print(state_fm_2002,end=" ")
In [407]:
df_state_2002 = {
"State":['A & N ISLANDS', 'ANDHRA PRADESH', 'ARUNACHAL PRADESH', 'ASSAM', 'BIHAR', 'CHANDIGARH', 'CHHATTISGARH',
'D & N HAVELI', 'DAMAN & DIU', 'DELHI (UT)', 'GOA', 'GUJARAT', 'HARYANA', 'HIMACHAL PRADESH', 'JAMMU & KASHMIR',
'JHARKHAND', 'KARNATAKA', 'KERALA', 'LAKSHADWEEP', 'MADHYA PRADESH', 'MAHARASHTRA', 'MANIPUR', 'MEGHALAYA',
'MIZORAM', 'NAGALAND', 'ODISHA', 'PUDUCHERRY', 'PUNJAB', 'RAJASTHAN', 'SIKKIM', 'TAMIL NADU', 'TRIPURA',
'UTTAR PRADESH', 'UTTARAKHAND', 'WEST BENGAL'],
"Total":[s2_19,s2_20,s2_21,s2_22,s2_23,s2_24,s2_25,s2_26,s2_27,s2_28,s2_29,s2_30,s2_31,s2_32,s2_33,s2_34,
s2_35,s2_36,s2_37,s2_38,s2_39,s2_40,s2_41,s2_42,s2_43,s2_44,s2_45,s2_46,s2_47,s2_48,s2_49,s2_50,
s2_51,s2_52,s2_53],
"Male":[t_ANm_2002, t_APm_2002, t_ARPm_2002, t_ASm_2002, t_BHm_2002, t_CHm_2002, t_CTm_2002, t_DNm_2002,
t_DDm_2002, t_DLm_2002, t_GOAm_2002, t_GJm_2002, t_HRm_2002, t_HPm_2002,t_JKm_2002, t_JHm_2002,
t_KNm_2002, t_KERm_2002, t_LDm_2002,t_MPm_2002,t_MHm_2002,t_MNm_2002,t_MGm_2002,t_MZm_2002,
t_NGm_2002, t_ODm_2002, t_PDm_2002,t_PBm_2002,t_RJm_2002,t_SMm_2002,t_TNm_2002,t_TPm_2002,
t_UPm_2002, t_UKm_2002, t_WBm_2002],
"Female":[t_ANfm_2002, t_APfm_2002, t_ARPfm_2002, t_ASfm_2002, t_BHfm_2002, t_CHfm_2002, t_CTfm_2002, t_DNfm_2002,
t_DDfm_2002, t_DLfm_2002, t_GOAfm_2002, t_GJfm_2002, t_HRfm_2002, t_HPfm_2002,t_JKfm_2002, t_JHfm_2002,
t_KNfm_2002, t_KERfm_2002, t_LDfm_2002,t_MPfm_2002,t_MHfm_2002,t_MNfm_2002,t_MGfm_2002,t_MZfm_2002,
t_NGfm_2002, t_ODfm_2002, t_PDfm_2002,t_PBfm_2002,t_RJfm_2002,t_SMfm_2002,t_TNfm_2002,t_TPfm_2002,
t_UPfm_2002, t_UKfm_2002, t_WBfm_2002]
}
df_All_st_2002 = pd.DataFrame(df_state_2002)
df_All_st_2002
Out[407]:
In [408]:
pivot_2002 = pd.pivot_table(df_All_st_2002, values=None, index='State', columns=None,
fill_value=None, margins=False, dropna=True, margins_name='All', observed=False)
pivot_2002
Out[408]:
In [409]:
pivot_2002.plot(kind='bar',figsize=(60,30))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("States",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths of Males And Females In Different State",fontsize=55)
plt.legend(["Female","Male","Total"], prop={"size":30})
plt.show
Out[409]:
In [410]:
cause_l_percent_2002=[]
for i in cause_l_2002:
p=i/np.sum(cause_l_2002)
avg_2002_cause=p*100
cause_l_percent_2002.append(avg_2002_cause)
print(cause_l_percent_2002,end=" ")
In [411]:
np.max(cause_l_percent_2002) #Married
Out[411]:
In [412]:
np.min(cause_l_percent_2002) #Ideological Causes/Hero Worshipping
Out[412]:
In [413]:
state_s_2002_percent=[]
tot=np.sum(state_s_2002)
for i in state_s_2002:
xy=i/tot
avg_state_2002=xy*100
state_s_2002_percent.append(avg_state_2002)
print(state_s_2002_percent,end=" ")
In [414]:
np.max(state_s_2002_percent) #Maharashtra
Out[414]:
In [415]:
np.min(state_s_2002_percent) #Lakshdweep
Out[415]:
In [416]:
l_age_grp_2002=[s2_4,s2_5,s2_6,s2_7,s2_8,s2_54]
l_age_grp_2002
Out[416]:
In [417]:
l_age_grp_2002_percent=[]
for i in l_age_grp_2002:
yz=i/s2_1
avg_2002_age_grp=yz*100
l_age_grp_2002_percent.append(avg_2002_age_grp)
l_age_grp_2002_percent
Out[417]:
In [418]:
np.max(l_age_grp_2002_percent) #0-100+
Out[418]:
In [419]:
np.min(l_age_grp_2002_percent) #0-14
Out[419]:
In [420]:
total_male_percent_2002= (s2_2/s2_1)*100
total_male_percent_2002
Out[420]:
In [421]:
total_female_percent_2002= (s2_3/s2_1)*100
total_female_percent_2002
Out[421]:
2003¶
In [422]:
df_2003 = suicide_df.loc[(suicide_df["Year"]==2003)]
df_2003
Out[422]:
In [423]:
t2003_dcount = df_2003.loc[:,"Total"]
s3_1 = np.sum(t2003_dcount)
s3_1
Out[423]:
In [424]:
male_2003 = df_2003.loc[(df_2003["Gender"]=="Male")]
male_2003
Out[424]:
In [425]:
m2003_count = male_2003.loc[:,"Total"]
s3_2 = np.sum(m2003_count)
s3_2
Out[425]:
In [426]:
female_2003 = df_2003.loc[(df_2003["Gender"]=="Female")]
female_2003
Out[426]:
In [427]:
fm2003_count = female_2003.loc[:,"Total"]
s3_3 = np.sum(fm2003_count)
s3_3
Out[427]:
In [428]:
df_age_d014_2003 = df_2003.loc[(df_2003["Age_group"]=="0-14")]
df_age_d014_2003
Out[428]:
In [429]:
t_014_2003 = df_age_d014_2003.loc[:,"Total"]
s3_4 = np.sum(t_014_2003)
s3_4
Out[429]:
In [430]:
df_age_d1529_2003 = df_2003.loc[(df_2003["Age_group"]=="15-29")]
df_age_d1529_2003
Out[430]:
In [431]:
t_1529_2003 = df_age_d1529_2003.loc[:,"Total"]
s3_5 = np.sum(t_1529_2003)
s3_5
Out[431]:
In [432]:
df_age_d3044_2003 = df_2003.loc[(df_2003["Age_group"]=="30-44")]
df_age_d3044_2003
Out[432]:
In [433]:
t_3044_2003 = df_age_d3044_2003.loc[:,"Total"]
s3_6 = np.sum(t_3044_2003)
s3_6
Out[433]:
In [434]:
df_age_d4559_2003 = df_2003.loc[(df_2003["Age_group"]=="45-59")]
df_age_d4559_2003
Out[434]:
In [435]:
t_4559_2003 = df_age_d4559_2003.loc[:,"Total"]
s3_7 = np.sum(t_4559_2003)
s3_7
Out[435]:
In [436]:
df_age_d60_2003 = df_2003.loc[(df_2003["Age_group"]=="60+")]
df_age_d60_2003
Out[436]:
In [437]:
t_60_2003 = df_age_d60_2003.loc[:,"Total"]
s3_8 = np.sum(t_60_2003)
s3_8
Out[437]:
In [438]:
df_age_d0100_2003 = df_2003.loc[(df_2003["Age_group"]=="0-100+")]
df_age_d0100_2003
Out[438]:
In [439]:
t_0100_2003 = df_age_d0100_2003.loc[:,"Total"]
s3_54 = np.sum(t_0100_2003)
s3_54
Out[439]:
In [440]:
df_014m_2003 = df_age_d014_2003.loc[(df_age_d014_2003["Gender"]=="Male")]
df_014m_2003
Out[440]:
In [441]:
t_014m_2003 = df_014m_2003.loc[:,"Total"]
s3_9 = np.sum(t_014m_2003)
s3_9
Out[441]:
In [442]:
df_014fm_2003 = df_age_d014_2003.loc[(df_age_d014_2003["Gender"]=="Female")]
df_014fm_2003
Out[442]:
In [443]:
t_014fm_2003 = df_014fm_2003.loc[:,"Total"]
s3_10 = np.sum(t_014fm_2003)
s3_10
Out[443]:
In [444]:
df_1529m_2003 = df_age_d1529_2003.loc[(df_age_d1529_2003["Gender"]=="Male")]
df_1529m_2003
Out[444]:
In [445]:
t_1529m_2003 = df_1529m_2003.loc[:,"Total"]
s3_11 = np.sum(t_1529m_2003)
s3_11
Out[445]:
In [446]:
df_1529fm_2003 = df_age_d1529_2003.loc[(df_age_d1529_2003["Gender"]=="Female")]
df_1529fm_2003
Out[446]:
In [447]:
t_1529fm_2003 = df_1529fm_2003.loc[:,"Total"]
s3_12 = np.sum(t_1529fm_2003)
s3_12
Out[447]:
In [448]:
df_3044m_2003 = df_age_d3044_2003.loc[(df_age_d3044_2003["Gender"]=="Male")]
df_3044m_2003
Out[448]:
In [449]:
t_3044m_2003 = df_3044m_2003.loc[:,"Total"]
s3_13 = np.sum(t_3044m_2003)
s3_13
Out[449]:
In [450]:
df_3044fm_2003 = df_age_d3044_2003.loc[(df_age_d3044_2003["Gender"]=="Female")]
df_3044fm_2003
Out[450]:
In [451]:
t_3044fm_2003 = df_3044fm_2003.loc[:,"Total"]
s3_14 = np.sum(t_3044fm_2003)
s3_14
Out[451]:
In [452]:
df_4559m_2003 = df_age_d4559_2003.loc[(df_age_d4559_2003["Gender"]=="Male")]
df_4559m_2003
Out[452]:
In [453]:
t_4559m_2003 = df_4559m_2003.loc[:,"Total"]
s3_15 = np.sum(t_4559m_2003)
s3_15
Out[453]:
In [454]:
df_4559fm_2003 = df_age_d4559_2003.loc[(df_age_d4559_2003["Gender"]=="Female")]
df_4559fm_2003
Out[454]:
In [455]:
t_4559fm_2003 = df_4559fm_2003.loc[:,"Total"]
s3_16 = np.sum(t_4559fm_2003)
s3_16
Out[455]:
In [456]:
df_60m_2003 = df_age_d60_2003.loc[(df_age_d60_2003["Gender"]=="Male")]
df_60m_2003
Out[456]:
In [457]:
t_60m_2003 = df_60m_2003.loc[:,"Total"]
s3_17 = np.sum(t_60m_2003)
s3_17
Out[457]:
In [458]:
df_60fm_2003 = df_age_d60_2003.loc[(df_age_d60_2003["Gender"]=="Female")]
df_60fm_2003
Out[458]:
In [459]:
t_60fm_2003 = df_60fm_2003.loc[:,"Total"]
s3_18 = np.sum(t_60fm_2003)
s3_18
Out[459]:
In [460]:
df_0100fm_2003 = df_age_d0100_2003.loc[(df_age_d0100_2003["Gender"]=="Female")]
df_0100fm_2003
Out[460]:
In [461]:
t_0100fm_2003 = df_0100fm_2003.loc[:,"Total"]
s3_55 = np.sum(t_0100fm_2003)
s3_55
Out[461]:
In [462]:
df_0100m_2003 = df_age_d0100_2003.loc[(df_age_d0100_2003["Gender"]=="Male")]
df_0100m_2003
Out[462]:
In [463]:
t_0100m_2003 = df_0100m_2003.loc[:,"Total"]
s3_56 = np.sum(t_0100m_2003)
s3_56
Out[463]:
In [464]:
df_st_AN_2003 = df_2003.loc[(df_2003["State"]=="A & N ISLANDS")]
df_st_AN_2003
Out[464]:
In [465]:
t_AN_2003 = df_st_AN_2003.loc[:,"Total"]
s3_19 = np.sum(t_AN_2003)
s3_19
Out[465]:
In [466]:
df_st_AP_2003 = df_2003.loc[(df_2003["State"]=="ANDHRA PRADESH")]
df_st_AP_2003
Out[466]:
In [467]:
t_AP_2003 = df_st_AP_2003.loc[:,"Total"]
s3_20 = np.sum(t_AP_2003)
s3_20
Out[467]:
In [468]:
df_st_ARP_2003 = df_2003.loc[(df_2003["State"]=="ARUNACHAL PRADESH")]
df_st_ARP_2003
Out[468]:
In [469]:
t_ARP_2003 = df_st_ARP_2003.loc[:,"Total"]
s3_21 = np.sum(t_ARP_2003)
s3_21
Out[469]:
In [470]:
df_st_AS_2003 = df_2003.loc[(df_2003["State"]=="ASSAM")]
df_st_AS_2003
Out[470]:
In [471]:
t_AS_2003 = df_st_AS_2003.loc[:,"Total"]
s3_22 = np.sum(t_AS_2003)
s3_22
Out[471]:
In [472]:
df_st_BH_2003 = df_2003.loc[(df_2003["State"]=="BIHAR")]
df_st_BH_2003
Out[472]:
In [473]:
t_BH_2003 = df_st_BH_2003.loc[:,"Total"]
s3_23 = np.sum(t_BH_2003)
s3_23
Out[473]:
In [474]:
df_st_CH_2003 = df_2003.loc[(df_2003["State"]=="CHANDIGARH")]
df_st_CH_2003
Out[474]:
In [475]:
t_CH_2003 = df_st_CH_2003.loc[:,"Total"]
s3_24 = np.sum(t_CH_2003)
s3_24
Out[475]:
In [476]:
df_st_CT_2003 = df_2003.loc[(df_2003["State"]=="CHHATTISGARH")]
df_st_CT_2003
Out[476]:
In [477]:
t_CT_2003 = df_st_CT_2003.loc[:,"Total"]
s3_25 = np.sum(t_CT_2003)
s3_25
Out[477]:
In [478]:
df_st_DN_2003 = df_2003.loc[(df_2003["State"]=="D & N HAVELI")]
df_st_DN_2003
Out[478]:
In [479]:
t_DN_2003 = df_st_DN_2003.loc[:,"Total"]
s3_26 = np.sum(t_DN_2003)
s3_26
Out[479]:
In [480]:
df_st_DD_2003 = df_2003.loc[(df_2003["State"]=="DAMAN & DIU")]
df_st_DD_2003
Out[480]:
In [481]:
t_DD_2003 = df_st_DD_2003.loc[:,"Total"]
s3_27 = np.sum(t_DD_2003)
s3_27
Out[481]:
In [482]:
df_st_DL_2003 = df_2003.loc[(df_2003["State"]=="DELHI (UT)")]
df_st_DL_2003
Out[482]:
In [483]:
t_DL_2003 = df_st_DL_2003.loc[:,"Total"]
s3_28 = np.sum(t_DL_2003)
s3_28
Out[483]:
In [484]:
df_st_GOA_2003 = df_2003.loc[(df_2003["State"]=="GOA")]
df_st_GOA_2003
Out[484]:
In [485]:
t_GOA_2003 = df_st_GOA_2003.loc[:,"Total"]
s3_29 = np.sum(t_GOA_2003)
s3_29
Out[485]:
In [486]:
df_st_GJ_2003 = df_2003.loc[(df_2003["State"]=="GUJARAT")]
df_st_GJ_2003
Out[486]:
In [487]:
t_GJ_2003 = df_st_GJ_2003.loc[:,"Total"]
s3_30 = np.sum(t_GJ_2003)
s3_30
Out[487]:
In [488]:
df_st_HR_2003 = df_2003.loc[(df_2003["State"]=="HARYANA")]
df_st_HR_2003
Out[488]:
In [489]:
t_HR_2003 = df_st_HR_2003.loc[:,"Total"]
s3_31 = np.sum(t_HR_2003)
s3_31
Out[489]:
In [490]:
df_st_HP_2003 = df_2003.loc[(df_2003["State"]=="HIMACHAL PRADESH")]
df_st_HP_2003
Out[490]:
In [491]:
t_HP_2003 = df_st_HP_2003.loc[:,"Total"]
s3_32 = np.sum(t_HP_2003)
s3_32
Out[491]:
In [492]:
df_st_JK_2003 = df_2003.loc[(df_2003["State"]=="JAMMU & KASHMIR")]
df_st_JK_2003
Out[492]:
In [493]:
t_JK_2003 = df_st_JK_2003.loc[:,"Total"]
s3_33 = np.sum(t_JK_2003)
s3_33
Out[493]:
In [494]:
df_st_JH_2003 = df_2003.loc[(df_2003["State"]=="JHARKHAND")]
df_st_JH_2003
Out[494]:
In [495]:
t_JH_2003 = df_st_JH_2003.loc[:,"Total"]
s3_34 = np.sum(t_JH_2003)
s3_34
Out[495]:
In [496]:
df_st_KN_2003 = df_2003.loc[(df_2003["State"]=="KARNATAKA")]
df_st_KN_2003
Out[496]:
In [497]:
t_KN_2003 = df_st_KN_2003.loc[:,"Total"]
s3_35 = np.sum(t_KN_2003)
s3_35
Out[497]:
In [498]:
df_st_KER_2003 = df_2003.loc[(df_2003["State"]=="KERALA")]
df_st_KER_2003
Out[498]:
In [499]:
t_KER_2003 = df_st_KER_2003.loc[:,"Total"]
s3_36 = np.sum(t_KER_2003)
s3_36
Out[499]:
In [500]:
df_st_LD_2003 = df_2003.loc[(df_2003["State"]=="LAKSHADWEEP")]
df_st_LD_2003
Out[500]:
In [501]:
t_LD_2003 = df_st_LD_2003.loc[:,"Total"]
s3_37 = np.sum(t_LD_2003)
s3_37
Out[501]:
In [502]:
df_st_MP_2003 = df_2003.loc[(df_2003["State"]=="MADHYA PRADESH")]
df_st_MP_2003
Out[502]:
In [503]:
t_MP_2003 = df_st_MP_2003.loc[:,"Total"]
s3_38 = np.sum(t_MP_2003)
s3_38
Out[503]:
In [504]:
df_st_MH_2003 = df_2003.loc[(df_2003["State"]=="MAHARASHTRA")]
df_st_MH_2003
Out[504]:
In [505]:
t_MH_2003 = df_st_MH_2003.loc[:,"Total"]
s3_39 = np.sum(t_MH_2003)
s3_39
Out[505]:
In [506]:
df_st_MN_2003 = df_2003.loc[(df_2003["State"]=="MANIPUR")]
df_st_MN_2003
Out[506]:
In [507]:
t_MN_2003 = df_st_MN_2003.loc[:,"Total"]
s3_40 = np.sum(t_MN_2003)
s3_40
Out[507]:
In [508]:
df_st_MG_2003 = df_2003.loc[(df_2003["State"]=="MEGHALAYA")]
df_st_MG_2003
Out[508]:
In [509]:
t_MG_2003 = df_st_MG_2003.loc[:,"Total"]
s3_41 = np.sum(t_MG_2003)
s3_41
Out[509]:
In [510]:
df_st_MZ_2003 = df_2003.loc[(df_2003["State"]=="MIZORAM")]
df_st_MZ_2003
Out[510]:
In [511]:
t_MN_2003 = df_st_MN_2003.loc[:,"Total"]
s3_42 = np.sum(t_MN_2003)
s3_42
Out[511]:
In [512]:
df_st_NG_2003 = df_2003.loc[(df_2003["State"]=="NAGALAND")]
df_st_NG_2003
Out[512]:
In [513]:
t_NG_2003 = df_st_NG_2003.loc[:,"Total"]
s3_43 = np.sum(t_NG_2003)
s3_43
Out[513]:
In [514]:
df_st_OD_2003 = df_2003.loc[(df_2003["State"]=="ODISHA")]
df_st_OD_2003
Out[514]:
In [515]:
t_OD_2003 = df_st_OD_2003.loc[:,"Total"]
s3_44 = np.sum(t_OD_2003)
s3_44
Out[515]:
In [516]:
df_st_PD_2003 = df_2003.loc[(df_2003["State"]=="PUDUCHERRY")]
df_st_PD_2003
Out[516]:
In [517]:
t_PD_2003 = df_st_PD_2003.loc[:,"Total"]
s3_45 = np.sum(t_PD_2003)
s3_45
Out[517]:
In [518]:
df_st_PB_2003 = df_2003.loc[(df_2003["State"]=="PUNJAB")]
df_st_PB_2003
Out[518]:
In [519]:
t_PB_2003 = df_st_PB_2003.loc[:,"Total"]
s3_46 = np.sum(t_PB_2003)
s3_46
Out[519]:
In [520]:
df_st_RJ_2003 = df_2003.loc[(df_2003["State"]=="RAJASTHAN")]
df_st_RJ_2003
Out[520]:
In [521]:
t_RJ_2003 = df_st_RJ_2003.loc[:,"Total"]
s3_47 = np.sum(t_RJ_2003)
s3_47
Out[521]:
In [522]:
df_st_SM_2003 = df_2003.loc[(df_2003["State"]=="SIKKIM")]
df_st_SM_2003
Out[522]:
In [523]:
t_SM_2003 = df_st_SM_2003.loc[:,"Total"]
s3_48 = np.sum(t_SM_2003)
s3_48
Out[523]:
In [524]:
df_st_TN_2003 = df_2003.loc[(df_2003["State"]=="TAMIL NADU")]
df_st_TN_2003
Out[524]:
In [525]:
t_TN_2003 = df_st_TN_2003.loc[:,"Total"]
s3_49 = np.sum(t_TN_2003)
s3_49
Out[525]:
In [526]:
df_st_TP_2003 = df_2003.loc[(df_2003["State"]=="TRIPURA")]
df_st_TP_2003
Out[526]:
In [527]:
t_TP_2003 = df_st_TP_2003.loc[:,"Total"]
s3_50 = np.sum(t_TP_2003)
s3_50
Out[527]:
In [528]:
df_st_UP_2003 = df_2003.loc[(df_2003["State"]=="UTTAR PRADESH")]
df_st_UP_2003
Out[528]:
In [529]:
t_UP_2003 = df_st_UP_2003.loc[:,"Total"]
s3_51 = np.sum(t_UP_2003)
s3_51
Out[529]:
In [530]:
df_st_UK_2003 = df_2003.loc[(df_2003["State"]=="UTTARAKHAND")]
df_st_UK_2003
Out[530]:
In [531]:
t_UK_2003 = df_st_UK_2003.loc[:,"Total"]
s3_52 = np.sum(t_UK_2003)
s3_52
Out[531]:
In [532]:
df_st_WB_2003 = df_2003.loc[(df_2003["State"]=="WEST BENGAL")]
df_st_WB_2003
Out[532]:
In [533]:
t_WB_2003 = df_st_WB_2003.loc[:,"Total"]
s3_53 = np.sum(t_WB_2003)
s3_53
Out[533]:
In [534]:
t_ANm_2003 = np.sum(df_st_AN_2003.where(df_st_AN_2003["Gender"]=="Male")["Total"])
t_ANm_2003
Out[534]:
In [535]:
t_ANfm_2003 = np.sum(df_st_AN_2003.where(df_st_AN_2003["Gender"]=="Female")["Total"])
t_ANfm_2003
Out[535]:
In [536]:
t_APm_2003 = np.sum(df_st_AP_2003.where(df_st_AP_2003["Gender"]=="Male")["Total"])
t_APm_2003
Out[536]:
In [537]:
t_APfm_2003 = np.sum(df_st_AP_2003.where(df_st_AP_2003["Gender"]=="Female")["Total"])
t_APfm_2003
Out[537]:
In [538]:
t_ARPm_2003 = np.sum(df_st_ARP_2003.where(df_st_ARP_2003["Gender"]=="Male")["Total"])
t_ARPm_2003
Out[538]:
In [539]:
t_ARPfm_2003 = np.sum(df_st_ARP_2003.where(df_st_ARP_2003["Gender"]=="Female")["Total"])
t_ARPfm_2003
Out[539]:
In [540]:
t_ASm_2003 = np.sum(df_st_AS_2003.where(df_st_AS_2003["Gender"]=="Male")["Total"])
t_ASm_2003
Out[540]:
In [541]:
t_ASfm_2003 = np.sum(df_st_AS_2003.where(df_st_AS_2003["Gender"]=="Female")["Total"])
t_ASfm_2003
Out[541]:
In [542]:
t_BHm_2003 = np.sum(df_st_BH_2003.where(df_st_BH_2003["Gender"]=="Male")["Total"])
t_BHm_2003
Out[542]:
In [543]:
t_BHfm_2003 = np.sum(df_st_BH_2003.where(df_st_BH_2003["Gender"]=="Female")["Total"])
t_BHfm_2003
Out[543]:
In [544]:
t_CHm_2003 = np.sum(df_st_CH_2003.where(df_st_CH_2003["Gender"]=="Male")["Total"])
t_CHm_2003
Out[544]:
In [545]:
t_CHfm_2003 = np.sum(df_st_CH_2003.where(df_st_CH_2003["Gender"]=="Female")["Total"])
t_CHfm_2003
Out[545]:
In [546]:
t_CTm_2003 = np.sum(df_st_CT_2003.where(df_st_CT_2003["Gender"]=="Male")["Total"])
t_CTm_2003
Out[546]:
In [547]:
t_CTfm_2003 = np.sum(df_st_CT_2003.where(df_st_CT_2003["Gender"]=="Female")["Total"])
t_CTfm_2003
Out[547]:
In [548]:
t_DNm_2003 = np.sum(df_st_DN_2003.where(df_st_DN_2003["Gender"]=="Male")["Total"])
t_DNm_2003
Out[548]:
In [549]:
t_DNfm_2003 = np.sum(df_st_DN_2003.where(df_st_DN_2003["Gender"]=="Female")["Total"])
t_DNfm_2003
Out[549]:
In [550]:
t_DDm_2003 = np.sum(df_st_DD_2003.where(df_st_DD_2003["Gender"]=="Male")["Total"])
t_DDm_2003
Out[550]:
In [551]:
t_DDfm_2003 = np.sum(df_st_DD_2003.where(df_st_DD_2003["Gender"]=="Female")["Total"])
t_DDfm_2003
Out[551]:
In [552]:
t_DLm_2003 = np.sum(df_st_DL_2003.where(df_st_DL_2003["Gender"]=="Male")["Total"])
t_DLm_2003
Out[552]:
In [553]:
t_DLfm_2003 = np.sum(df_st_DL_2003.where(df_st_DL_2003["Gender"]=="Female")["Total"])
t_DLfm_2003
Out[553]:
In [554]:
t_GOAm_2003 = np.sum(df_st_GOA_2003.where(df_st_GOA_2003["Gender"]=="Male")["Total"])
t_GOAm_2003
Out[554]:
In [555]:
t_GOAfm_2003 = np.sum(df_st_GOA_2003.where(df_st_GOA_2003["Gender"]=="Female")["Total"])
t_GOAfm_2003
Out[555]:
In [556]:
t_GJm_2003 = np.sum(df_st_GJ_2003.where(df_st_GJ_2003["Gender"]=="Male")["Total"])
t_GJm_2003
Out[556]:
In [557]:
t_GJfm_2003 = np.sum(df_st_GJ_2003.where(df_st_GJ_2003["Gender"]=="Female")["Total"])
t_GJfm_2003
Out[557]:
In [558]:
t_HRm_2003 = np.sum(df_st_HR_2003.where(df_st_HR_2003["Gender"]=="Male")["Total"])
t_HRm_2003
Out[558]:
In [559]:
t_HRfm_2003 = np.sum(df_st_HR_2003.where(df_st_HR_2003["Gender"]=="Female")["Total"])
t_HRfm_2003
Out[559]:
In [560]:
t_HPm_2003 = np.sum(df_st_HP_2003.where(df_st_HP_2003["Gender"]=="Male")["Total"])
t_HPm_2003
Out[560]:
In [561]:
t_HPfm_2003 = np.sum(df_st_HP_2003.where(df_st_HP_2003["Gender"]=="Female")["Total"])
t_HPfm_2003
Out[561]:
In [562]:
t_JKm_2003 = np.sum(df_st_JK_2003.where(df_st_JK_2003["Gender"]=="Male")["Total"])
t_JKm_2003
Out[562]:
In [563]:
t_JKfm_2003 = np.sum(df_st_JK_2003.where(df_st_JK_2003["Gender"]=="Female")["Total"])
t_JKfm_2003
Out[563]:
In [564]:
t_JHm_2003 = np.sum(df_st_JH_2003.where(df_st_JH_2003["Gender"]=="Male")["Total"])
t_JHm_2003
Out[564]:
In [565]:
t_JHfm_2003 = np.sum(df_st_JH_2003.where(df_st_JH_2003["Gender"]=="Female")["Total"])
t_JHfm_2003
Out[565]:
In [566]:
t_KNm_2003 = np.sum(df_st_KN_2003.where(df_st_KN_2003["Gender"]=="Male")["Total"])
t_KNm_2003
Out[566]:
In [567]:
t_KNfm_2003 = np.sum(df_st_KN_2003.where(df_st_KN_2003["Gender"]=="Female")["Total"])
t_KNfm_2003
Out[567]:
In [568]:
t_KERm_2003 = np.sum(df_st_KER_2003.where(df_st_KER_2003["Gender"]=="Male")["Total"])
t_KERm_2003
Out[568]:
In [569]:
t_KERfm_2003 = np.sum(df_st_KER_2003.where(df_st_KER_2003["Gender"]=="Female")["Total"])
t_KERfm_2003
Out[569]:
In [570]:
t_LDm_2003 = np.sum(df_st_LD_2003.where(df_st_LD_2003["Gender"]=="Male")["Total"])
t_LDm_2003
Out[570]:
In [571]:
t_LDfm_2003 = np.sum(df_st_LD_2003.where(df_st_LD_2003["Gender"]=="Female")["Total"])
t_LDfm_2003
Out[571]:
In [572]:
t_MPm_2003 = np.sum(df_st_MP_2003.where(df_st_MP_2003["Gender"]=="Male")["Total"])
t_MPm_2003
Out[572]:
In [573]:
t_MPfm_2003 = np.sum(df_st_MP_2003.where(df_st_MP_2003["Gender"]=="Female")["Total"])
t_MPfm_2003
Out[573]:
In [574]:
t_MHm_2003 = np.sum(df_st_MH_2003.where(df_st_MH_2003["Gender"]=="Male")["Total"])
t_MHm_2003
Out[574]:
In [575]:
t_MHfm_2003 = np.sum(df_st_MH_2003.where(df_st_MH_2003["Gender"]=="Female")["Total"])
t_MHfm_2003
Out[575]:
In [576]:
t_MNm_2003 = np.sum(df_st_MN_2003.where(df_st_MN_2003["Gender"]=="Male")["Total"])
t_MNm_2003
Out[576]:
In [577]:
t_MNfm_2003 = np.sum(df_st_MN_2003.where(df_st_MN_2003["Gender"]=="Female")["Total"])
t_MNfm_2003
Out[577]:
In [578]:
t_MGm_2003 = np.sum(df_st_MG_2003.where(df_st_MG_2003["Gender"]=="Male")["Total"])
t_MGm_2003
Out[578]:
In [579]:
t_MGfm_2003 = np.sum(df_st_MG_2003.where(df_st_MG_2003["Gender"]=="Female")["Total"])
t_MGfm_2003
Out[579]:
In [580]:
t_MZm_2003 = np.sum(df_st_MZ_2003.where(df_st_MZ_2003["Gender"]=="Male")["Total"])
t_MZm_2003
Out[580]:
In [581]:
t_MZfm_2003 = np.sum(df_st_MZ_2003.where(df_st_MZ_2003["Gender"]=="Female")["Total"])
t_MZfm_2003
Out[581]:
In [582]:
t_NGm_2003 = np.sum(df_st_NG_2003.where(df_st_NG_2003["Gender"]=="Male")["Total"])
t_NGm_2003
Out[582]:
In [583]:
t_NGfm_2003 = np.sum(df_st_NG_2003.where(df_st_NG_2003["Gender"]=="Female")["Total"])
t_MGfm_2003
Out[583]:
In [584]:
t_ODm_2003 = np.sum(df_st_OD_2003.where(df_st_OD_2003["Gender"]=="Male")["Total"])
t_ODm_2003
Out[584]:
In [585]:
t_ODfm_2003 = np.sum(df_st_OD_2003.where(df_st_OD_2003["Gender"]=="Female")["Total"])
t_ODfm_2003
Out[585]:
In [586]:
t_PDm_2003 = np.sum(df_st_PD_2003.where(df_st_PD_2003["Gender"]=="Male")["Total"])
t_PDm_2003
Out[586]:
In [587]:
t_PDfm_2003 = np.sum(df_st_PD_2003.where(df_st_PD_2003["Gender"]=="Female")["Total"])
t_PDfm_2003
Out[587]:
In [588]:
t_PBm_2003 = np.sum(df_st_PB_2003.where(df_st_PB_2003["Gender"]=="Male")["Total"])
t_PBm_2003
Out[588]:
In [589]:
t_PBfm_2003 = np.sum(df_st_PB_2003.where(df_st_PB_2003["Gender"]=="Female")["Total"])
t_PBfm_2003
Out[589]:
In [590]:
t_RJm_2003 = np.sum(df_st_RJ_2003.where(df_st_RJ_2003["Gender"]=="Male")["Total"])
t_RJm_2003
Out[590]:
In [591]:
t_RJfm_2003 = np.sum(df_st_RJ_2003.where(df_st_RJ_2003["Gender"]=="Female")["Total"])
t_RJfm_2003
Out[591]:
In [592]:
t_SMm_2003 = np.sum(df_st_SM_2003.where(df_st_SM_2003["Gender"]=="Male")["Total"])
t_SMm_2003
Out[592]:
In [593]:
t_SMfm_2003 = np.sum(df_st_SM_2003.where(df_st_SM_2003["Gender"]=="Female")["Total"])
t_SMfm_2003
Out[593]:
In [594]:
t_TNm_2003 = np.sum(df_st_TN_2003.where(df_st_TN_2003["Gender"]=="Male")["Total"])
t_TNm_2003
Out[594]:
In [595]:
t_TNfm_2003 = np.sum(df_st_TN_2003.where(df_st_TN_2003["Gender"]=="Female")["Total"])
t_TNfm_2003
Out[595]:
In [596]:
t_TPm_2003 = np.sum(df_st_TP_2003.where(df_st_TP_2003["Gender"]=="Male")["Total"])
t_TPm_2003
Out[596]:
In [597]:
t_TPfm_2003 = np.sum(df_st_TP_2003.where(df_st_TP_2003["Gender"]=="Female")["Total"])
t_TPfm_2003
Out[597]:
In [598]:
t_UPm_2003 = np.sum(df_st_UP_2003.where(df_st_UP_2003["Gender"]=="Male")["Total"])
t_UPm_2003
Out[598]:
In [599]:
t_UPfm_2003 = np.sum(df_st_UP_2003.where(df_st_UP_2003["Gender"]=="Female")["Total"])
t_UPfm_2003
Out[599]:
In [600]:
t_UKm_2003 = np.sum(df_st_UK_2003.where(df_st_UK_2003["Gender"]=="Male")["Total"])
t_UKm_2003
Out[600]:
In [601]:
t_UKfm_2003 = np.sum(df_st_UK_2003.where(df_st_UK_2003["Gender"]=="Female")["Total"])
t_UKfm_2003
Out[601]:
In [602]:
t_WBm_2003 = np.sum(df_st_WB_2003.where(df_st_WB_2003["Gender"]=="Male")["Total"])
t_WBm_2003
Out[602]:
In [603]:
t_WBfm_2003 = np.sum(df_st_WB_2003.where(df_st_WB_2003["Gender"]=="Female")["Total"])
t_WBfm_2003
Out[603]:
In [604]:
l_2003=list(df_2003.loc[:,"Type"].unique())
l_2003.sort()
print(l_2003,end=" ")
In [605]:
cause_l_2003=[]
for i in l_2003:
rough= np.sum(df_2003.where(df_2003["Type"]==i)["Total"])
cause_l_2003.append(rough)
print(cause_l_2003,end=" ")
In [606]:
x1_2003 = set(df_2003.loc[:,"State"])
x1_2003
len(x1_2003)
Out[606]:
In [607]:
cause_df_2003 = pd.DataFrame(cause_l_2003,l_2003)
cause_df_2003.reset_index()
Out[607]:
In [608]:
cause_df_2003.plot.bar(stacked=True,figsize=(50,20))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("Causes",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths due to Different Causes in 2003",fontsize=55)
plt.legend(["Death Count"], prop={"size":30})
Out[608]:
In [609]:
s_2003=list(df_2003.loc[:,"State"].unique())
s_2003.sort()
print(s_2003,end=" ")
In [610]:
state_s_2003 = [s3_19,s3_20,s3_21,s3_22,s3_23,s3_24,s3_25,s3_26,s3_27,s3_28,s3_29,s3_30,s3_31,s3_32,s3_33,s3_34,
s3_35,s3_36,s3_37,s3_38,s3_39,s3_40,s3_41,s3_42,s3_43,s3_44,s3_45,s3_46,s3_47,s3_48,s3_49,s3_50,
s3_51,s3_52,s3_53]
print(state_s_2003,end=" ")
In [611]:
state_m_2003=[t_ANm_2003, t_APm_2003, t_ARPm_2003, t_ASm_2003, t_BHm_2003, t_CHm_2003, t_CTm_2003, t_DNm_2003,
t_DDm_2003, t_DLm_2003, t_GOAm_2003, t_GJm_2003, t_HRm_2003, t_HPm_2003,t_JKm_2003, t_JHm_2003,
t_KNm_2003, t_KERm_2003, t_LDm_2003,t_MPm_2003,t_MHm_2003,t_MNm_2003,t_MGm_2003,t_MZm_2003,
t_NGm_2003, t_ODm_2003, t_PDm_2003,t_PBm_2003,t_RJm_2003,t_SMm_2003,t_TNm_2003,t_TPm_2003,
t_UPm_2003, t_UKm_2003, t_WBm_2003]
print(state_m_2003,end=" ")
In [612]:
state_fm_2003=[t_ANfm_2003, t_APfm_2003, t_ARPfm_2003, t_ASfm_2003, t_BHfm_2003, t_CHfm_2003, t_CTfm_2003, t_DNfm_2003,
t_DDfm_2003, t_DLfm_2003, t_GOAfm_2003, t_GJfm_2003, t_HRfm_2003, t_HPfm_2003,t_JKfm_2003, t_JHfm_2003,
t_KNfm_2003, t_KERfm_2003, t_LDfm_2003,t_MPfm_2003,t_MHfm_2003,t_MNfm_2003,t_MGfm_2003,t_MZfm_2003,
t_NGfm_2003, t_ODfm_2003, t_PDfm_2003,t_PBfm_2003,t_RJfm_2003,t_SMfm_2003,t_TNfm_2003,t_TPfm_2003,
t_UPfm_2003, t_UKfm_2003, t_WBfm_2003]
print(state_fm_2003,end=" ")
In [613]:
df_state_2003 = {
"State":['A & N ISLANDS', 'ANDHRA PRADESH', 'ARUNACHAL PRADESH', 'ASSAM', 'BIHAR', 'CHANDIGARH', 'CHHATTISGARH',
'D & N HAVELI', 'DAMAN & DIU', 'DELHI (UT)', 'GOA', 'GUJARAT', 'HARYANA', 'HIMACHAL PRADESH', 'JAMMU & KASHMIR',
'JHARKHAND', 'KARNATAKA', 'KERALA', 'LAKSHADWEEP', 'MADHYA PRADESH', 'MAHARASHTRA', 'MANIPUR', 'MEGHALAYA',
'MIZORAM', 'NAGALAND', 'ODISHA', 'PUDUCHERRY', 'PUNJAB', 'RAJASTHAN', 'SIKKIM', 'TAMIL NADU', 'TRIPURA',
'UTTAR PRADESH', 'UTTARAKHAND', 'WEST BENGAL'],
"Total":[s3_19,s3_20,s3_21,s3_22,s3_23,s3_24,s3_25,s3_26,s3_27,s3_28,s3_29,s3_30,s3_31,s3_32,s3_33,s3_34,
s3_35,s3_36,s3_37,s3_38,s3_39,s3_40,s3_41,s3_42,s3_43,s3_44,s3_45,s3_46,s3_47,s3_48,s3_49,s3_50,
s3_51,s3_52,s3_53],
"Male":[t_ANm_2003, t_APm_2003, t_ARPm_2003, t_ASm_2003, t_BHm_2003, t_CHm_2003, t_CTm_2003, t_DNm_2003,
t_DDm_2003, t_DLm_2003, t_GOAm_2003, t_GJm_2003, t_HRm_2003, t_HPm_2003,t_JKm_2003, t_JHm_2003,
t_KNm_2003, t_KERm_2003, t_LDm_2003,t_MPm_2003,t_MHm_2003,t_MNm_2003,t_MGm_2003,t_MZm_2003,
t_NGm_2003, t_ODm_2003, t_PDm_2003,t_PBm_2003,t_RJm_2003,t_SMm_2003,t_TNm_2003,t_TPm_2003,
t_UPm_2003, t_UKm_2003, t_WBm_2003],
"Female":[t_ANfm_2003, t_APfm_2003, t_ARPfm_2003, t_ASfm_2003, t_BHfm_2003, t_CHfm_2003, t_CTfm_2003, t_DNfm_2003,
t_DDfm_2003, t_DLfm_2003, t_GOAfm_2003, t_GJfm_2003, t_HRfm_2003, t_HPfm_2003,t_JKfm_2003, t_JHfm_2003,
t_KNfm_2003, t_KERfm_2003, t_LDfm_2003,t_MPfm_2003,t_MHfm_2003,t_MNfm_2003,t_MGfm_2003,t_MZfm_2003,
t_NGfm_2003, t_ODfm_2003, t_PDfm_2003,t_PBfm_2003,t_RJfm_2003,t_SMfm_2003,t_TNfm_2003,t_TPfm_2003,
t_UPfm_2003, t_UKfm_2003, t_WBfm_2003]
}
df_All_st_2003 = pd.DataFrame(df_state_2003)
df_All_st_2003
Out[613]:
In [614]:
pivot_2003 = pd.pivot_table(df_All_st_2003, values=None, index='State', columns=None,
fill_value=None, margins=False, dropna=True, margins_name='All', observed=False)
pivot_2003
Out[614]:
In [615]:
pivot_2003.plot(kind='bar',figsize=(60,30))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("States",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths of Males And Females In Different State",fontsize=55)
plt.legend(["Female","Male","Total"], prop={"size":30})
plt.show
Out[615]:
In [616]:
cause_l_percent_2003=[]
for i in cause_l_2003:
p=i/np.sum(cause_l_2003)
avg_2003_cause=p*100
cause_l_percent_2003.append(avg_2003_cause)
print(cause_l_percent_2003,end=" ")
In [617]:
np.max(cause_l_percent_2003) #Married
Out[617]:
In [618]:
np.min(cause_l_percent_2003) #Ideological Causes/Hero Worshipping
Out[618]:
In [619]:
state_s_2003_percent=[]
tot=np.sum(state_s_2003)
for i in state_s_2003:
xy=i/tot
avg_state_2003=xy*100
state_s_2003_percent.append(avg_state_2003)
print(state_s_2003_percent,end=" ")
In [620]:
np.max(state_s_2003_percent) #Maharashtra
Out[620]:
In [621]:
np.min(state_s_2003_percent) #Lakshdweep
Out[621]:
In [622]:
l_age_grp_2003=[s3_4,s3_5,s3_6,s3_7,s3_8,s3_54]
l_age_grp_2003
Out[622]:
In [623]:
l_age_grp_2003_percent=[]
for i in l_age_grp_2003:
yz=i/s3_1
avg_2003_age_grp=yz*100
l_age_grp_2003_percent.append(avg_2003_age_grp)
l_age_grp_2003_percent
Out[623]:
In [624]:
np.max(l_age_grp_2003_percent) #0-100+
Out[624]:
In [625]:
np.min(l_age_grp_2003_percent) #0-14
Out[625]:
In [626]:
total_male_percent_2003= (s3_2/s3_1)*100
total_male_percent_2003
Out[626]:
In [627]:
total_female_percent_2003= (s3_3/s3_1)*100
total_female_percent_2003
Out[627]:
In [ ]:
2004¶
In [628]:
df_2004 = suicide_df.loc[(suicide_df["Year"]==2004)]
df_2004
Out[628]:
In [629]:
t2004_dcount = df_2004.loc[:,"Total"]
s4_1 = np.sum(t2004_dcount)
s4_1
Out[629]:
In [630]:
male_2004 = df_2004.loc[(df_2004["Gender"]=="Male")]
male_2004
Out[630]:
In [631]:
m2004_count = male_2004.loc[:,"Total"]
s4_2 = np.sum(m2004_count)
s4_2
Out[631]:
In [632]:
female_2004 = df_2004.loc[(df_2004["Gender"]=="Female")]
female_2004
Out[632]:
In [633]:
fm2004_count = female_2004.loc[:,"Total"]
s4_3 = np.sum(fm2004_count)
s4_3
Out[633]:
In [634]:
df_age_d014_2004 = df_2004.loc[(df_2004["Age_group"]=="0-14")]
df_age_d014_2004
Out[634]:
In [635]:
t_014_2004 = df_age_d014_2004.loc[:,"Total"]
s4_4 = np.sum(t_014_2004)
s4_4
Out[635]:
In [636]:
df_age_d1529_2004 = df_2004.loc[(df_2004["Age_group"]=="15-29")]
df_age_d1529_2004
Out[636]:
In [637]:
t_1529_2004 = df_age_d1529_2004.loc[:,"Total"]
s4_5 = np.sum(t_1529_2004)
s4_5
Out[637]:
In [638]:
df_age_d3044_2004 = df_2004.loc[(df_2004["Age_group"]=="30-44")]
df_age_d3044_2004
Out[638]:
In [639]:
t_3044_2004 = df_age_d3044_2004.loc[:,"Total"]
s4_6 = np.sum(t_3044_2004)
s4_6
Out[639]:
In [640]:
df_age_d4559_2004 = df_2004.loc[(df_2004["Age_group"]=="45-59")]
df_age_d4559_2004
Out[640]:
In [641]:
t_4559_2004 = df_age_d4559_2004.loc[:,"Total"]
s4_7 = np.sum(t_4559_2004)
s4_7
Out[641]:
In [642]:
df_age_d60_2004 = df_2004.loc[(df_2004["Age_group"]=="60+")]
df_age_d60_2004
Out[642]:
In [643]:
t_60_2004 = df_age_d60_2004.loc[:,"Total"]
s4_8 = np.sum(t_60_2004)
s4_8
Out[643]:
In [644]:
df_age_d0100_2004 = df_2004.loc[(df_2004["Age_group"]=="0-100+")]
df_age_d0100_2004
Out[644]:
In [645]:
t_0100_2004 = df_age_d0100_2004.loc[:,"Total"]
s4_54 = np.sum(t_0100_2004)
s4_54
Out[645]:
In [646]:
df_014m_2004 = df_age_d014_2004.loc[(df_age_d014_2004["Gender"]=="Male")]
df_014m_2004
Out[646]:
In [647]:
t_014m_2004 = df_014m_2004.loc[:,"Total"]
s4_9 = np.sum(t_014m_2004)
s4_9
Out[647]:
In [648]:
df_014fm_2004 = df_age_d014_2004.loc[(df_age_d014_2004["Gender"]=="Female")]
df_014fm_2004
Out[648]:
In [649]:
t_014fm_2004 = df_014fm_2004.loc[:,"Total"]
s4_10 = np.sum(t_014fm_2004)
s4_10
Out[649]:
In [650]:
df_1529m_2004 = df_age_d1529_2004.loc[(df_age_d1529_2004["Gender"]=="Male")]
df_1529m_2004
Out[650]:
In [651]:
t_1529m_2004 = df_1529m_2004.loc[:,"Total"]
s4_11 = np.sum(t_1529m_2004)
s4_11
Out[651]:
In [652]:
df_1529fm_2004 = df_age_d1529_2004.loc[(df_age_d1529_2004["Gender"]=="Female")]
df_1529fm_2004
Out[652]:
In [653]:
t_1529fm_2004 = df_1529fm_2004.loc[:,"Total"]
s4_12 = np.sum(t_1529fm_2004)
s4_12
Out[653]:
In [654]:
df_3044m_2004 = df_age_d3044_2004.loc[(df_age_d3044_2004["Gender"]=="Male")]
df_3044m_2004
Out[654]:
In [655]:
t_3044m_2004 = df_3044m_2004.loc[:,"Total"]
s4_13 = np.sum(t_3044m_2004)
s4_13
Out[655]:
In [656]:
df_3044fm_2004 = df_age_d3044_2004.loc[(df_age_d3044_2004["Gender"]=="Female")]
df_3044fm_2004
Out[656]:
In [657]:
t_3044fm_2004 = df_3044fm_2004.loc[:,"Total"]
s4_14 = np.sum(t_3044fm_2004)
s4_14
Out[657]:
In [658]:
df_4559m_2004 = df_age_d4559_2004.loc[(df_age_d4559_2004["Gender"]=="Male")]
df_4559m_2004
Out[658]:
In [659]:
t_4559m_2004 = df_4559m_2004.loc[:,"Total"]
s4_15 = np.sum(t_4559m_2004)
s4_15
Out[659]:
In [660]:
df_4559fm_2004 = df_age_d4559_2004.loc[(df_age_d4559_2004["Gender"]=="Female")]
df_4559fm_2004
Out[660]:
In [661]:
t_4559fm_2004 = df_4559fm_2004.loc[:,"Total"]
s4_16 = np.sum(t_4559fm_2004)
s4_16
Out[661]:
In [662]:
df_60m_2004 = df_age_d60_2004.loc[(df_age_d60_2004["Gender"]=="Male")]
df_60m_2004
Out[662]:
In [663]:
t_60m_2004 = df_60m_2004.loc[:,"Total"]
s4_17 = np.sum(t_60m_2004)
s4_17
Out[663]:
In [664]:
df_60fm_2004 = df_age_d60_2004.loc[(df_age_d60_2004["Gender"]=="Female")]
df_60fm_2004
Out[664]:
In [665]:
t_60fm_2004 = df_60fm_2004.loc[:,"Total"]
s4_18 = np.sum(t_60fm_2004)
s4_18
Out[665]:
In [666]:
df_0100fm_2004 = df_age_d0100_2004.loc[(df_age_d0100_2004["Gender"]=="Female")]
df_0100fm_2004
Out[666]:
In [667]:
t_0100fm_2004 = df_0100fm_2004.loc[:,"Total"]
s4_55 = np.sum(t_0100fm_2004)
s4_55
Out[667]:
In [668]:
df_0100m_2004 = df_age_d0100_2004.loc[(df_age_d0100_2004["Gender"]=="Male")]
df_0100m_2004
Out[668]:
In [669]:
t_0100m_2004 = df_0100m_2004.loc[:,"Total"]
s4_56 = np.sum(t_0100m_2004)
s4_56
Out[669]:
In [670]:
df_st_AN_2004 = df_2004.loc[(df_2004["State"]=="A & N ISLANDS")]
df_st_AN_2004
Out[670]:
In [671]:
t_AN_2004 = df_st_AN_2004.loc[:,"Total"]
s4_19 = np.sum(t_AN_2004)
s4_19
Out[671]:
In [672]:
df_st_AP_2004 = df_2004.loc[(df_2004["State"]=="ANDHRA PRADESH")]
df_st_AP_2004
Out[672]:
In [673]:
t_AP_2004 = df_st_AP_2004.loc[:,"Total"]
s4_20 = np.sum(t_AP_2004)
s4_20
Out[673]:
In [674]:
df_st_ARP_2004 = df_2004.loc[(df_2004["State"]=="ARUNACHAL PRADESH")]
df_st_ARP_2004
Out[674]:
In [675]:
t_ARP_2004 = df_st_ARP_2004.loc[:,"Total"]
s4_21 = np.sum(t_ARP_2004)
s4_21
Out[675]:
In [676]:
df_st_AS_2004 = df_2004.loc[(df_2004["State"]=="ASSAM")]
df_st_AS_2004
Out[676]:
In [677]:
t_AS_2004 = df_st_AS_2004.loc[:,"Total"]
s4_22 = np.sum(t_AS_2004)
s4_22
Out[677]:
In [678]:
df_st_BH_2004 = df_2004.loc[(df_2004["State"]=="BIHAR")]
df_st_BH_2004
Out[678]:
In [679]:
t_BH_2004 = df_st_BH_2004.loc[:,"Total"]
s4_23 = np.sum(t_BH_2004)
s4_23
Out[679]:
In [680]:
df_st_CH_2004 = df_2004.loc[(df_2004["State"]=="CHANDIGARH")]
df_st_CH_2004
Out[680]:
In [681]:
t_CH_2004 = df_st_CH_2004.loc[:,"Total"]
s4_24 = np.sum(t_CH_2004)
s4_24
Out[681]:
In [682]:
df_st_CT_2004 = df_2004.loc[(df_2004["State"]=="CHHATTISGARH")]
df_st_CT_2004
Out[682]:
In [683]:
t_CT_2004 = df_st_CT_2004.loc[:,"Total"]
s4_25 = np.sum(t_CT_2004)
s4_25
Out[683]:
In [684]:
df_st_DN_2004 = df_2004.loc[(df_2004["State"]=="D & N HAVELI")]
df_st_DN_2004
Out[684]:
In [685]:
t_DN_2004 = df_st_DN_2004.loc[:,"Total"]
s4_26 = np.sum(t_DN_2004)
s4_26
Out[685]:
In [686]:
df_st_DD_2004 = df_2004.loc[(df_2004["State"]=="DAMAN & DIU")]
df_st_DD_2004
Out[686]:
In [687]:
t_DD_2004 = df_st_DD_2004.loc[:,"Total"]
s4_27 = np.sum(t_DD_2004)
s4_27
Out[687]:
In [688]:
df_st_DL_2004 = df_2004.loc[(df_2004["State"]=="DELHI (UT)")]
df_st_DL_2004
Out[688]:
In [689]:
t_DL_2004 = df_st_DL_2004.loc[:,"Total"]
s4_28 = np.sum(t_DL_2004)
s4_28
Out[689]:
In [690]:
df_st_GOA_2004 = df_2004.loc[(df_2004["State"]=="GOA")]
df_st_GOA_2004
Out[690]:
In [691]:
t_GOA_2004 = df_st_GOA_2004.loc[:,"Total"]
s4_29 = np.sum(t_GOA_2004)
s4_29
Out[691]:
In [692]:
df_st_GJ_2004 = df_2004.loc[(df_2004["State"]=="GUJARAT")]
df_st_GJ_2004
Out[692]:
In [693]:
t_GJ_2004 = df_st_GJ_2004.loc[:,"Total"]
s4_30 = np.sum(t_GJ_2004)
s4_30
Out[693]:
In [694]:
df_st_HR_2004 = df_2004.loc[(df_2004["State"]=="HARYANA")]
df_st_HR_2004
Out[694]:
In [695]:
t_HR_2004 = df_st_HR_2004.loc[:,"Total"]
s4_31 = np.sum(t_HR_2004)
s4_31
Out[695]:
In [696]:
df_st_HP_2004 = df_2004.loc[(df_2004["State"]=="HIMACHAL PRADESH")]
df_st_HP_2004
Out[696]:
In [697]:
t_HP_2004 = df_st_HP_2004.loc[:,"Total"]
s4_32 = np.sum(t_HP_2004)
s4_32
Out[697]:
In [698]:
df_st_JK_2004 = df_2004.loc[(df_2004["State"]=="JAMMU & KASHMIR")]
df_st_JK_2004
Out[698]:
In [699]:
t_JK_2004 = df_st_JK_2004.loc[:,"Total"]
s4_33 = np.sum(t_JK_2004)
s4_33
Out[699]:
In [700]:
df_st_JH_2004 = df_2004.loc[(df_2004["State"]=="JHARKHAND")]
df_st_JH_2004
Out[700]:
In [701]:
t_JH_2004 = df_st_JH_2004.loc[:,"Total"]
s4_34 = np.sum(t_JH_2004)
s4_34
Out[701]:
In [702]:
df_st_KN_2004 = df_2004.loc[(df_2004["State"]=="KARNATAKA")]
df_st_KN_2004
Out[702]:
In [703]:
t_KN_2004 = df_st_KN_2004.loc[:,"Total"]
s4_35 = np.sum(t_KN_2004)
s4_35
Out[703]:
In [704]:
df_st_KER_2004 = df_2004.loc[(df_2004["State"]=="KERALA")]
df_st_KER_2004
Out[704]:
In [705]:
t_KER_2004 = df_st_KER_2004.loc[:,"Total"]
s4_36 = np.sum(t_KER_2004)
s4_36
Out[705]:
In [706]:
df_st_LD_2004 = df_2004.loc[(df_2004["State"]=="LAKSHADWEEP")]
df_st_LD_2004
Out[706]:
In [707]:
t_LD_2004 = df_st_LD_2004.loc[:,"Total"]
s4_37 = np.sum(t_LD_2004)
s4_37
Out[707]:
In [708]:
df_st_MP_2004 = df_2004.loc[(df_2004["State"]=="MADHYA PRADESH")]
df_st_MP_2004
Out[708]:
In [709]:
t_MP_2004 = df_st_MP_2004.loc[:,"Total"]
s4_38 = np.sum(t_MP_2004)
s4_38
Out[709]:
In [710]:
df_st_MH_2004 = df_2004.loc[(df_2004["State"]=="MAHARASHTRA")]
df_st_MH_2004
Out[710]:
In [711]:
t_MH_2004 = df_st_MH_2004.loc[:,"Total"]
s4_39 = np.sum(t_MH_2004)
s4_39
Out[711]:
In [712]:
df_st_MN_2004 = df_2004.loc[(df_2004["State"]=="MANIPUR")]
df_st_MN_2004
Out[712]:
In [713]:
t_MN_2004 = df_st_MN_2004.loc[:,"Total"]
s4_40 = np.sum(t_MN_2004)
s4_40
Out[713]:
In [714]:
df_st_MG_2004 = df_2004.loc[(df_2004["State"]=="MEGHALAYA")]
df_st_MG_2004
Out[714]:
In [715]:
t_MG_2004 = df_st_MG_2004.loc[:,"Total"]
s4_41 = np.sum(t_MG_2004)
s4_41
Out[715]:
In [716]:
df_st_MZ_2004 = df_2004.loc[(df_2004["State"]=="MIZORAM")]
df_st_MZ_2004
Out[716]:
In [717]:
t_MN_2004 = df_st_MN_2004.loc[:,"Total"]
s4_42 = np.sum(t_MN_2004)
s4_42
Out[717]:
In [718]:
df_st_NG_2004 = df_2004.loc[(df_2004["State"]=="NAGALAND")]
df_st_NG_2004
Out[718]:
In [719]:
t_NG_2004 = df_st_NG_2004.loc[:,"Total"]
s4_43 = np.sum(t_NG_2004)
s4_43
Out[719]:
In [720]:
df_st_OD_2004 = df_2004.loc[(df_2004["State"]=="ODISHA")]
df_st_OD_2004
Out[720]:
In [721]:
t_OD_2004 = df_st_OD_2004.loc[:,"Total"]
s4_44 = np.sum(t_OD_2004)
s4_44
Out[721]:
In [722]:
df_st_PD_2004 = df_2004.loc[(df_2004["State"]=="PUDUCHERRY")]
df_st_PD_2004
Out[722]:
In [723]:
t_PD_2004 = df_st_PD_2004.loc[:,"Total"]
s4_45 = np.sum(t_PD_2004)
s4_45
Out[723]:
In [724]:
df_st_PB_2004 = df_2004.loc[(df_2004["State"]=="PUNJAB")]
df_st_PB_2004
Out[724]:
In [725]:
t_PB_2004 = df_st_PB_2004.loc[:,"Total"]
s4_46 = np.sum(t_PB_2004)
s4_46
Out[725]:
In [726]:
df_st_RJ_2004 = df_2004.loc[(df_2004["State"]=="RAJASTHAN")]
df_st_RJ_2004
Out[726]:
In [727]:
t_RJ_2004 = df_st_RJ_2004.loc[:,"Total"]
s4_47 = np.sum(t_RJ_2004)
s4_47
Out[727]:
In [728]:
df_st_SM_2004 = df_2004.loc[(df_2004["State"]=="SIKKIM")]
df_st_SM_2004
Out[728]:
In [729]:
t_SM_2004 = df_st_SM_2004.loc[:,"Total"]
s4_48 = np.sum(t_SM_2004)
s4_48
Out[729]:
In [730]:
df_st_TN_2004 = df_2004.loc[(df_2004["State"]=="TAMIL NADU")]
df_st_TN_2004
Out[730]:
In [731]:
t_TN_2004 = df_st_TN_2004.loc[:,"Total"]
s4_49 = np.sum(t_TN_2004)
s4_49
Out[731]:
In [732]:
df_st_TP_2004 = df_2004.loc[(df_2004["State"]=="TRIPURA")]
df_st_TP_2004
Out[732]:
In [733]:
t_TP_2004 = df_st_TP_2004.loc[:,"Total"]
s4_50 = np.sum(t_TP_2004)
s4_50
Out[733]:
In [734]:
df_st_UP_2004 = df_2004.loc[(df_2004["State"]=="UTTAR PRADESH")]
df_st_UP_2004
Out[734]:
In [735]:
t_UP_2004 = df_st_UP_2004.loc[:,"Total"]
s4_51 = np.sum(t_UP_2004)
s4_51
Out[735]:
In [736]:
df_st_UK_2004 = df_2004.loc[(df_2004["State"]=="UTTARAKHAND")]
df_st_UK_2004
Out[736]:
In [737]:
t_UK_2004 = df_st_UK_2004.loc[:,"Total"]
s4_52 = np.sum(t_UK_2004)
s4_52
Out[737]:
In [738]:
df_st_WB_2004 = df_2004.loc[(df_2004["State"]=="WEST BENGAL")]
df_st_WB_2004
Out[738]:
In [739]:
t_WB_2004 = df_st_WB_2004.loc[:,"Total"]
s4_53 = np.sum(t_WB_2004)
s4_53
Out[739]:
In [740]:
t_ANm_2004 = np.sum(df_st_AN_2004.where(df_st_AN_2004["Gender"]=="Male")["Total"])
t_ANm_2004
Out[740]:
In [741]:
t_ANfm_2004 = np.sum(df_st_AN_2004.where(df_st_AN_2004["Gender"]=="Female")["Total"])
t_ANfm_2004
Out[741]:
In [742]:
t_APm_2004 = np.sum(df_st_AP_2004.where(df_st_AP_2004["Gender"]=="Male")["Total"])
t_APm_2004
Out[742]:
In [743]:
t_APfm_2004 = np.sum(df_st_AP_2004.where(df_st_AP_2004["Gender"]=="Female")["Total"])
t_APfm_2004
Out[743]:
In [744]:
t_ARPm_2004 = np.sum(df_st_ARP_2004.where(df_st_ARP_2004["Gender"]=="Male")["Total"])
t_ARPm_2004
Out[744]:
In [745]:
t_ARPfm_2004 = np.sum(df_st_ARP_2004.where(df_st_ARP_2004["Gender"]=="Female")["Total"])
t_ARPfm_2004
Out[745]:
In [746]:
t_ASm_2004 = np.sum(df_st_AS_2004.where(df_st_AS_2004["Gender"]=="Male")["Total"])
t_ASm_2004
Out[746]:
In [747]:
t_ASfm_2004 = np.sum(df_st_AS_2004.where(df_st_AS_2004["Gender"]=="Female")["Total"])
t_ASfm_2004
Out[747]:
In [748]:
t_BHm_2004 = np.sum(df_st_BH_2004.where(df_st_BH_2004["Gender"]=="Male")["Total"])
t_BHm_2004
Out[748]:
In [749]:
t_BHfm_2004 = np.sum(df_st_BH_2004.where(df_st_BH_2004["Gender"]=="Female")["Total"])
t_BHfm_2004
Out[749]:
In [750]:
t_CHm_2004 = np.sum(df_st_CH_2004.where(df_st_CH_2004["Gender"]=="Male")["Total"])
t_CHm_2004
Out[750]:
In [751]:
t_CHfm_2004 = np.sum(df_st_CH_2004.where(df_st_CH_2004["Gender"]=="Female")["Total"])
t_CHfm_2004
Out[751]:
In [752]:
t_CTm_2004 = np.sum(df_st_CT_2004.where(df_st_CT_2004["Gender"]=="Male")["Total"])
t_CTm_2004
Out[752]:
In [753]:
t_CTfm_2004 = np.sum(df_st_CT_2004.where(df_st_CT_2004["Gender"]=="Female")["Total"])
t_CTfm_2004
Out[753]:
In [754]:
t_DNm_2004 = np.sum(df_st_DN_2004.where(df_st_DN_2004["Gender"]=="Male")["Total"])
t_DNm_2004
Out[754]:
In [755]:
t_DNfm_2004 = np.sum(df_st_DN_2004.where(df_st_DN_2004["Gender"]=="Female")["Total"])
t_DNfm_2004
Out[755]:
In [756]:
t_DDm_2004 = np.sum(df_st_DD_2004.where(df_st_DD_2004["Gender"]=="Male")["Total"])
t_DDm_2004
Out[756]:
In [757]:
t_DDfm_2004 = np.sum(df_st_DD_2004.where(df_st_DD_2004["Gender"]=="Female")["Total"])
t_DDfm_2004
Out[757]:
In [758]:
t_DLm_2004 = np.sum(df_st_DL_2004.where(df_st_DL_2004["Gender"]=="Male")["Total"])
t_DLm_2004
Out[758]:
In [759]:
t_DLfm_2004 = np.sum(df_st_DL_2004.where(df_st_DL_2004["Gender"]=="Female")["Total"])
t_DLfm_2004
Out[759]:
In [760]:
t_GOAm_2004 = np.sum(df_st_GOA_2004.where(df_st_GOA_2004["Gender"]=="Male")["Total"])
t_GOAm_2004
Out[760]:
In [761]:
t_GOAfm_2004 = np.sum(df_st_GOA_2004.where(df_st_GOA_2004["Gender"]=="Female")["Total"])
t_GOAfm_2004
Out[761]:
In [762]:
t_GJm_2004 = np.sum(df_st_GJ_2004.where(df_st_GJ_2004["Gender"]=="Male")["Total"])
t_GJm_2004
Out[762]:
In [763]:
t_GJfm_2004 = np.sum(df_st_GJ_2004.where(df_st_GJ_2004["Gender"]=="Female")["Total"])
t_GJfm_2004
Out[763]:
In [764]:
t_HRm_2004 = np.sum(df_st_HR_2004.where(df_st_HR_2004["Gender"]=="Male")["Total"])
t_HRm_2004
Out[764]:
In [765]:
t_HRfm_2004 = np.sum(df_st_HR_2004.where(df_st_HR_2004["Gender"]=="Female")["Total"])
t_HRfm_2004
Out[765]:
In [766]:
t_HPm_2004 = np.sum(df_st_HP_2004.where(df_st_HP_2004["Gender"]=="Male")["Total"])
t_HPm_2004
Out[766]:
In [767]:
t_HPfm_2004 = np.sum(df_st_HP_2004.where(df_st_HP_2004["Gender"]=="Female")["Total"])
t_HPfm_2004
Out[767]:
In [768]:
t_JKm_2004 = np.sum(df_st_JK_2004.where(df_st_JK_2004["Gender"]=="Male")["Total"])
t_JKm_2004
Out[768]:
In [769]:
t_JKfm_2004 = np.sum(df_st_JK_2004.where(df_st_JK_2004["Gender"]=="Female")["Total"])
t_JKfm_2004
Out[769]:
In [770]:
t_JHm_2004 = np.sum(df_st_JH_2004.where(df_st_JH_2004["Gender"]=="Male")["Total"])
t_JHm_2004
Out[770]:
In [771]:
t_JHfm_2004 = np.sum(df_st_JH_2004.where(df_st_JH_2004["Gender"]=="Female")["Total"])
t_JHfm_2004
Out[771]:
In [772]:
t_KNm_2004 = np.sum(df_st_KN_2004.where(df_st_KN_2004["Gender"]=="Male")["Total"])
t_KNm_2004
Out[772]:
In [773]:
t_KNfm_2004 = np.sum(df_st_KN_2004.where(df_st_KN_2004["Gender"]=="Female")["Total"])
t_KNfm_2004
Out[773]:
In [774]:
t_KERm_2004 = np.sum(df_st_KER_2004.where(df_st_KER_2004["Gender"]=="Male")["Total"])
t_KERm_2004
Out[774]:
In [775]:
t_KERfm_2004 = np.sum(df_st_KER_2004.where(df_st_KER_2004["Gender"]=="Female")["Total"])
t_KERfm_2004
Out[775]:
In [776]:
t_LDm_2004 = np.sum(df_st_LD_2004.where(df_st_LD_2004["Gender"]=="Male")["Total"])
t_LDm_2004
Out[776]:
In [777]:
t_LDfm_2004 = np.sum(df_st_LD_2004.where(df_st_LD_2004["Gender"]=="Female")["Total"])
t_LDfm_2004
Out[777]:
In [778]:
t_MPm_2004 = np.sum(df_st_MP_2004.where(df_st_MP_2004["Gender"]=="Male")["Total"])
t_MPm_2004
Out[778]:
In [779]:
t_MPfm_2004 = np.sum(df_st_MP_2004.where(df_st_MP_2004["Gender"]=="Female")["Total"])
t_MPfm_2004
Out[779]:
In [780]:
t_MHm_2004 = np.sum(df_st_MH_2004.where(df_st_MH_2004["Gender"]=="Male")["Total"])
t_MHm_2004
Out[780]:
In [781]:
t_MHfm_2004 = np.sum(df_st_MH_2004.where(df_st_MH_2004["Gender"]=="Female")["Total"])
t_MHfm_2004
Out[781]:
In [782]:
t_MNm_2004 = np.sum(df_st_MN_2004.where(df_st_MN_2004["Gender"]=="Male")["Total"])
t_MNm_2004
Out[782]:
In [783]:
t_MNfm_2004 = np.sum(df_st_MN_2004.where(df_st_MN_2004["Gender"]=="Female")["Total"])
t_MNfm_2004
Out[783]:
In [784]:
t_MGm_2004 = np.sum(df_st_MG_2004.where(df_st_MG_2004["Gender"]=="Male")["Total"])
t_MGm_2004
Out[784]:
In [785]:
t_MGfm_2004 = np.sum(df_st_MG_2004.where(df_st_MG_2004["Gender"]=="Female")["Total"])
t_MGfm_2004
Out[785]:
In [786]:
t_MZm_2004 = np.sum(df_st_MZ_2004.where(df_st_MZ_2004["Gender"]=="Male")["Total"])
t_MZm_2004
Out[786]:
In [787]:
t_MZfm_2004 = np.sum(df_st_MZ_2004.where(df_st_MZ_2004["Gender"]=="Female")["Total"])
t_MZfm_2004
Out[787]:
In [788]:
t_NGm_2004 = np.sum(df_st_NG_2004.where(df_st_NG_2004["Gender"]=="Male")["Total"])
t_NGm_2004
Out[788]:
In [789]:
t_NGfm_2004 = np.sum(df_st_NG_2004.where(df_st_NG_2004["Gender"]=="Female")["Total"])
t_MGfm_2004
Out[789]:
In [790]:
t_ODm_2004 = np.sum(df_st_OD_2004.where(df_st_OD_2004["Gender"]=="Male")["Total"])
t_ODm_2004
Out[790]:
In [791]:
t_ODfm_2004 = np.sum(df_st_OD_2004.where(df_st_OD_2004["Gender"]=="Female")["Total"])
t_ODfm_2004
Out[791]:
In [792]:
t_PDm_2004 = np.sum(df_st_PD_2004.where(df_st_PD_2004["Gender"]=="Male")["Total"])
t_PDm_2004
Out[792]:
In [793]:
t_PDfm_2004 = np.sum(df_st_PD_2004.where(df_st_PD_2004["Gender"]=="Female")["Total"])
t_PDfm_2004
Out[793]:
In [794]:
t_PBm_2004 = np.sum(df_st_PB_2004.where(df_st_PB_2004["Gender"]=="Male")["Total"])
t_PBm_2004
Out[794]:
In [795]:
t_PBfm_2004 = np.sum(df_st_PB_2004.where(df_st_PB_2004["Gender"]=="Female")["Total"])
t_PBfm_2004
Out[795]:
In [796]:
t_RJm_2004 = np.sum(df_st_RJ_2004.where(df_st_RJ_2004["Gender"]=="Male")["Total"])
t_RJm_2004
Out[796]:
In [797]:
t_RJfm_2004 = np.sum(df_st_RJ_2004.where(df_st_RJ_2004["Gender"]=="Female")["Total"])
t_RJfm_2004
Out[797]:
In [798]:
t_SMm_2004 = np.sum(df_st_SM_2004.where(df_st_SM_2004["Gender"]=="Male")["Total"])
t_SMm_2004
Out[798]:
In [799]:
t_SMfm_2004 = np.sum(df_st_SM_2004.where(df_st_SM_2004["Gender"]=="Female")["Total"])
t_SMfm_2004
Out[799]:
In [800]:
t_TNm_2004 = np.sum(df_st_TN_2004.where(df_st_TN_2004["Gender"]=="Male")["Total"])
t_TNm_2004
Out[800]:
In [801]:
t_TNfm_2004 = np.sum(df_st_TN_2004.where(df_st_TN_2004["Gender"]=="Female")["Total"])
t_TNfm_2004
Out[801]:
In [802]:
t_TPm_2004 = np.sum(df_st_TP_2004.where(df_st_TP_2004["Gender"]=="Male")["Total"])
t_TPm_2004
Out[802]:
In [803]:
t_TPfm_2004 = np.sum(df_st_TP_2004.where(df_st_TP_2004["Gender"]=="Female")["Total"])
t_TPfm_2004
Out[803]:
In [804]:
t_UPm_2004 = np.sum(df_st_UP_2004.where(df_st_UP_2004["Gender"]=="Male")["Total"])
t_UPm_2004
Out[804]:
In [805]:
t_UPfm_2004 = np.sum(df_st_UP_2004.where(df_st_UP_2004["Gender"]=="Female")["Total"])
t_UPfm_2004
Out[805]:
In [806]:
t_UKm_2004 = np.sum(df_st_UK_2004.where(df_st_UK_2004["Gender"]=="Male")["Total"])
t_UKm_2004
Out[806]:
In [807]:
t_UKfm_2004 = np.sum(df_st_UK_2004.where(df_st_UK_2004["Gender"]=="Female")["Total"])
t_UKfm_2004
Out[807]:
In [808]:
t_WBm_2004 = np.sum(df_st_WB_2004.where(df_st_WB_2004["Gender"]=="Male")["Total"])
t_WBm_2004
Out[808]:
In [809]:
t_WBfm_2004 = np.sum(df_st_WB_2004.where(df_st_WB_2004["Gender"]=="Female")["Total"])
t_WBfm_2004
Out[809]:
In [810]:
l_2004=list(df_2004.loc[:,"Type"].unique())
l_2004.sort()
print(l_2004,end=" ")
In [811]:
cause_l_2004=[]
for i in l_2004:
rough= np.sum(df_2004.where(df_2004["Type"]==i)["Total"])
cause_l_2004.append(rough)
print(cause_l_2004,end=" ")
In [812]:
x1_2004 = set(df_2004.loc[:,"State"])
x1_2004
len(x1_2004)
Out[812]:
In [813]:
cause_df_2004 = pd.DataFrame(cause_l_2004,l_2004)
cause_df_2004.reset_index()
Out[813]:
In [814]:
cause_df_2004.plot.bar(stacked=True,figsize=(50,20))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("Causes",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths due to Different Causes in 2004",fontsize=55)
plt.legend(["Death Count"], prop={"size":30})
Out[814]:
In [815]:
s_2004=list(df_2004.loc[:,"State"].unique())
s_2004.sort()
print(s_2004,end=" ")
In [816]:
state_s_2004 = [s4_19,s4_20,s4_21,s4_22,s4_23,s4_24,s4_25,s4_26,s4_27,s4_28,s4_29,s4_30,s4_31,s4_32,s4_33,s4_34,
s4_35,s4_36,s4_37,s4_38,s4_39,s4_40,s4_41,s4_42,s4_43,s4_44,s4_45,s4_46,s4_47,s4_48,s4_49,s4_50,
s4_51,s4_52,s4_53]
print(state_s_2004,end=" ")
In [817]:
state_m_2004=[t_ANm_2004, t_APm_2004, t_ARPm_2004, t_ASm_2004, t_BHm_2004, t_CHm_2004, t_CTm_2004, t_DNm_2004,
t_DDm_2004, t_DLm_2004, t_GOAm_2004, t_GJm_2004, t_HRm_2004, t_HPm_2004,t_JKm_2004, t_JHm_2004,
t_KNm_2004, t_KERm_2004, t_LDm_2004,t_MPm_2004,t_MHm_2004,t_MNm_2004,t_MGm_2004,t_MZm_2004,
t_NGm_2004, t_ODm_2004, t_PDm_2004,t_PBm_2004,t_RJm_2004,t_SMm_2004,t_TNm_2004,t_TPm_2004,
t_UPm_2004, t_UKm_2004, t_WBm_2004]
print(state_m_2004,end=" ")
In [818]:
state_fm_2004=[t_ANfm_2004, t_APfm_2004, t_ARPfm_2004, t_ASfm_2004, t_BHfm_2004, t_CHfm_2004, t_CTfm_2004, t_DNfm_2004,
t_DDfm_2004, t_DLfm_2004, t_GOAfm_2004, t_GJfm_2004, t_HRfm_2004, t_HPfm_2004,t_JKfm_2004, t_JHfm_2004,
t_KNfm_2004, t_KERfm_2004, t_LDfm_2004,t_MPfm_2004,t_MHfm_2004,t_MNfm_2004,t_MGfm_2004,t_MZfm_2004,
t_NGfm_2004, t_ODfm_2004, t_PDfm_2004,t_PBfm_2004,t_RJfm_2004,t_SMfm_2004,t_TNfm_2004,t_TPfm_2004,
t_UPfm_2004, t_UKfm_2004, t_WBfm_2004]
print(state_fm_2004,end=" ")
In [819]:
df_state_2004 = {
"State":['A & N ISLANDS', 'ANDHRA PRADESH', 'ARUNACHAL PRADESH', 'ASSAM', 'BIHAR', 'CHANDIGARH', 'CHHATTISGARH',
'D & N HAVELI', 'DAMAN & DIU', 'DELHI (UT)', 'GOA', 'GUJARAT', 'HARYANA', 'HIMACHAL PRADESH', 'JAMMU & KASHMIR',
'JHARKHAND', 'KARNATAKA', 'KERALA', 'LAKSHADWEEP', 'MADHYA PRADESH', 'MAHARASHTRA', 'MANIPUR', 'MEGHALAYA',
'MIZORAM', 'NAGALAND', 'ODISHA', 'PUDUCHERRY', 'PUNJAB', 'RAJASTHAN', 'SIKKIM', 'TAMIL NADU', 'TRIPURA',
'UTTAR PRADESH', 'UTTARAKHAND', 'WEST BENGAL'],
"Total":[s4_19,s4_20,s4_21,s4_22,s4_23,s4_24,s4_25,s4_26,s4_27,s4_28,s4_29,s4_30,s4_31,s4_32,s4_33,s4_34,
s4_35,s4_36,s4_37,s4_38,s4_39,s4_40,s4_41,s4_42,s4_43,s4_44,s4_45,s4_46,s4_47,s4_48,s4_49,s4_50,
s4_51,s4_52,s4_53],
"Male":[t_ANm_2004, t_APm_2004, t_ARPm_2004, t_ASm_2004, t_BHm_2004, t_CHm_2004, t_CTm_2004, t_DNm_2004,
t_DDm_2004, t_DLm_2004, t_GOAm_2004, t_GJm_2004, t_HRm_2004, t_HPm_2004,t_JKm_2004, t_JHm_2004,
t_KNm_2004, t_KERm_2004, t_LDm_2004,t_MPm_2004,t_MHm_2004,t_MNm_2004,t_MGm_2004,t_MZm_2004,
t_NGm_2004, t_ODm_2004, t_PDm_2004,t_PBm_2004,t_RJm_2004,t_SMm_2004,t_TNm_2004,t_TPm_2004,
t_UPm_2004, t_UKm_2004, t_WBm_2004],
"Female":[t_ANfm_2004, t_APfm_2004, t_ARPfm_2004, t_ASfm_2004, t_BHfm_2004, t_CHfm_2004, t_CTfm_2004, t_DNfm_2004,
t_DDfm_2004, t_DLfm_2004, t_GOAfm_2004, t_GJfm_2004, t_HRfm_2004, t_HPfm_2004,t_JKfm_2004, t_JHfm_2004,
t_KNfm_2004, t_KERfm_2004, t_LDfm_2004,t_MPfm_2004,t_MHfm_2004,t_MNfm_2004,t_MGfm_2004,t_MZfm_2004,
t_NGfm_2004, t_ODfm_2004, t_PDfm_2004,t_PBfm_2004,t_RJfm_2004,t_SMfm_2004,t_TNfm_2004,t_TPfm_2004,
t_UPfm_2004, t_UKfm_2004, t_WBfm_2004]
}
df_All_st_2004 = pd.DataFrame(df_state_2004)
df_All_st_2004
Out[819]:
In [820]:
pivot_2004 = pd.pivot_table(df_All_st_2004, values=None, index='State', columns=None,
fill_value=None, margins=False, dropna=True, margins_name='All', observed=False)
pivot_2004
Out[820]:
In [821]:
pivot_2004.plot(kind='bar',figsize=(60,30))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("States",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths of Males And Females In Different State",fontsize=55)
plt.legend(["Female","Male","Total"], prop={"size":30})
plt.show
Out[821]:
In [822]:
cause_l_percent_2004=[]
for i in cause_l_2004:
p=i/np.sum(cause_l_2004)
avg_2004_cause=p*100
cause_l_percent_2004.append(avg_2004_cause)
print(cause_l_percent_2004,end=" ")
In [823]:
np.max(cause_l_percent_2004) #Married
Out[823]:
In [824]:
np.min(cause_l_percent_2004) #Ideological Causes/Hero Worshipping
Out[824]:
In [825]:
state_s_2004_percent=[]
tot=np.sum(state_s_2004)
for i in state_s_2004:
xy=i/tot
avg_state_2004=xy*100
state_s_2004_percent.append(avg_state_2004)
print(state_s_2004_percent,end=" ")
In [826]:
np.max(state_s_2004_percent) #Maharashtra
Out[826]:
In [827]:
np.min(state_s_2004_percent) #Lakshdweep
Out[827]:
In [828]:
l_age_grp_2004=[s4_4,s4_5,s4_6,s4_7,s4_8,s4_54]
l_age_grp_2004
Out[828]:
In [829]:
l_age_grp_2004_percent=[]
for i in l_age_grp_2004:
yz=i/s4_1
avg_2004_age_grp=yz*100
l_age_grp_2004_percent.append(avg_2004_age_grp)
l_age_grp_2004_percent
Out[829]:
In [830]:
np.max(l_age_grp_2004_percent) #0-100+
Out[830]:
In [831]:
np.min(l_age_grp_2004_percent) #0-14
Out[831]:
In [832]:
total_male_percent_2004= (s4_2/s4_1)*100
total_male_percent_2004
Out[832]:
In [833]:
total_female_percent_2004= (s4_3/s4_1)*100
total_female_percent_2004
Out[833]:
2005¶
In [834]:
df_2005 = suicide_df.loc[(suicide_df["Year"]==2005)]
df_2005
Out[834]:
In [835]:
t2005_dcount = df_2005.loc[:,"Total"]
s5_1 = np.sum(t2005_dcount)
s5_1
Out[835]:
In [836]:
male_2005 = df_2005.loc[(df_2005["Gender"]=="Male")]
male_2005
Out[836]:
In [837]:
m2005_count = male_2005.loc[:,"Total"]
s5_2 = np.sum(m2005_count)
s5_2
Out[837]:
In [838]:
female_2005 = df_2005.loc[(df_2005["Gender"]=="Female")]
female_2005
Out[838]:
In [839]:
fm2005_count = female_2005.loc[:,"Total"]
s5_3 = np.sum(fm2005_count)
s5_3
Out[839]:
In [840]:
df_age_d014_2005 = df_2005.loc[(df_2005["Age_group"]=="0-14")]
df_age_d014_2005
Out[840]:
In [841]:
t_014_2005 = df_age_d014_2005.loc[:,"Total"]
s5_4 = np.sum(t_014_2005)
s5_4
Out[841]:
In [842]:
df_age_d1529_2005 = df_2005.loc[(df_2005["Age_group"]=="15-29")]
df_age_d1529_2005
Out[842]:
In [843]:
t_1529_2005 = df_age_d1529_2005.loc[:,"Total"]
s5_5 = np.sum(t_1529_2005)
s5_5
Out[843]:
In [844]:
df_age_d3044_2005 = df_2005.loc[(df_2005["Age_group"]=="30-44")]
df_age_d3044_2005
Out[844]:
In [845]:
t_3044_2005 = df_age_d3044_2005.loc[:,"Total"]
s5_6 = np.sum(t_3044_2005)
s5_6
Out[845]:
In [846]:
df_age_d4559_2005 = df_2005.loc[(df_2005["Age_group"]=="45-59")]
df_age_d4559_2005
Out[846]:
In [847]:
t_4559_2005 = df_age_d4559_2005.loc[:,"Total"]
s5_7 = np.sum(t_4559_2005)
s5_7
Out[847]:
In [848]:
df_age_d60_2005 = df_2005.loc[(df_2005["Age_group"]=="60+")]
df_age_d60_2005
Out[848]:
In [849]:
t_60_2005 = df_age_d60_2005.loc[:,"Total"]
s5_8 = np.sum(t_60_2005)
s5_8
Out[849]:
In [850]:
df_age_d0100_2005 = df_2005.loc[(df_2005["Age_group"]=="0-100+")]
df_age_d0100_2005
Out[850]:
In [851]:
t_0100_2005 = df_age_d0100_2005.loc[:,"Total"]
s5_54 = np.sum(t_0100_2005)
s5_54
Out[851]:
In [852]:
df_014m_2005 = df_age_d014_2005.loc[(df_age_d014_2005["Gender"]=="Male")]
df_014m_2005
Out[852]:
In [853]:
t_014m_2005 = df_014m_2005.loc[:,"Total"]
s5_9 = np.sum(t_014m_2005)
s5_9
Out[853]:
In [854]:
df_014fm_2005 = df_age_d014_2005.loc[(df_age_d014_2005["Gender"]=="Female")]
df_014fm_2005
Out[854]:
In [855]:
t_014fm_2005 = df_014fm_2005.loc[:,"Total"]
s5_10 = np.sum(t_014fm_2005)
s5_10
Out[855]:
In [856]:
df_1529m_2005 = df_age_d1529_2005.loc[(df_age_d1529_2005["Gender"]=="Male")]
df_1529m_2005
Out[856]:
In [857]:
t_1529m_2005 = df_1529m_2005.loc[:,"Total"]
s5_11 = np.sum(t_1529m_2005)
s5_11
Out[857]:
In [858]:
df_1529fm_2005 = df_age_d1529_2005.loc[(df_age_d1529_2005["Gender"]=="Female")]
df_1529fm_2005
Out[858]:
In [859]:
t_1529fm_2005 = df_1529fm_2005.loc[:,"Total"]
s5_12 = np.sum(t_1529fm_2005)
s5_12
Out[859]:
In [860]:
df_3044m_2005 = df_age_d3044_2005.loc[(df_age_d3044_2005["Gender"]=="Male")]
df_3044m_2005
Out[860]:
In [861]:
t_3044m_2005 = df_3044m_2005.loc[:,"Total"]
s5_13 = np.sum(t_3044m_2005)
s5_13
Out[861]:
In [862]:
df_3044fm_2005 = df_age_d3044_2005.loc[(df_age_d3044_2005["Gender"]=="Female")]
df_3044fm_2005
Out[862]:
In [863]:
t_3044fm_2005 = df_3044fm_2005.loc[:,"Total"]
s5_14 = np.sum(t_3044fm_2005)
s5_14
Out[863]:
In [864]:
df_4559m_2005 = df_age_d4559_2005.loc[(df_age_d4559_2005["Gender"]=="Male")]
df_4559m_2005
Out[864]:
In [865]:
t_4559m_2005 = df_4559m_2005.loc[:,"Total"]
s5_15 = np.sum(t_4559m_2005)
s5_15
Out[865]:
In [866]:
df_4559fm_2005 = df_age_d4559_2005.loc[(df_age_d4559_2005["Gender"]=="Female")]
df_4559fm_2005
Out[866]:
In [867]:
t_4559fm_2005 = df_4559fm_2005.loc[:,"Total"]
s5_16 = np.sum(t_4559fm_2005)
s5_16
Out[867]:
In [868]:
df_60m_2005 = df_age_d60_2005.loc[(df_age_d60_2005["Gender"]=="Male")]
df_60m_2005
Out[868]:
In [869]:
t_60m_2005 = df_60m_2005.loc[:,"Total"]
s5_17 = np.sum(t_60m_2005)
s5_17
Out[869]:
In [870]:
df_60fm_2005 = df_age_d60_2005.loc[(df_age_d60_2005["Gender"]=="Female")]
df_60fm_2005
Out[870]:
In [871]:
t_60fm_2005 = df_60fm_2005.loc[:,"Total"]
s5_18 = np.sum(t_60fm_2005)
s5_18
Out[871]:
In [872]:
df_0100fm_2005 = df_age_d0100_2005.loc[(df_age_d0100_2005["Gender"]=="Female")]
df_0100fm_2005
Out[872]:
In [873]:
t_0100fm_2005 = df_0100fm_2005.loc[:,"Total"]
s5_55 = np.sum(t_0100fm_2005)
s5_55
Out[873]:
In [874]:
df_0100m_2005 = df_age_d0100_2005.loc[(df_age_d0100_2005["Gender"]=="Male")]
df_0100m_2005
Out[874]:
In [875]:
t_0100m_2005 = df_0100m_2005.loc[:,"Total"]
s5_56 = np.sum(t_0100m_2005)
s5_56
Out[875]:
In [876]:
df_st_AN_2005 = df_2005.loc[(df_2005["State"]=="A & N ISLANDS")]
df_st_AN_2005
Out[876]:
In [877]:
t_AN_2005 = df_st_AN_2005.loc[:,"Total"]
s5_19 = np.sum(t_AN_2005)
s5_19
Out[877]:
In [878]:
df_st_AP_2005 = df_2005.loc[(df_2005["State"]=="ANDHRA PRADESH")]
df_st_AP_2005
Out[878]:
In [879]:
t_AP_2005 = df_st_AP_2005.loc[:,"Total"]
s5_20 = np.sum(t_AP_2005)
s5_20
Out[879]:
In [880]:
df_st_ARP_2005 = df_2005.loc[(df_2005["State"]=="ARUNACHAL PRADESH")]
df_st_ARP_2005
Out[880]:
In [881]:
t_ARP_2005 = df_st_ARP_2005.loc[:,"Total"]
s5_21 = np.sum(t_ARP_2005)
s5_21
Out[881]:
In [882]:
df_st_AS_2005 = df_2005.loc[(df_2005["State"]=="ASSAM")]
df_st_AS_2005
Out[882]:
In [883]:
t_AS_2005 = df_st_AS_2005.loc[:,"Total"]
s5_22 = np.sum(t_AS_2005)
s5_22
Out[883]:
In [884]:
df_st_BH_2005 = df_2005.loc[(df_2005["State"]=="BIHAR")]
df_st_BH_2005
Out[884]:
In [885]:
t_BH_2005 = df_st_BH_2005.loc[:,"Total"]
s5_23 = np.sum(t_BH_2005)
s5_23
Out[885]:
In [886]:
df_st_CH_2005 = df_2005.loc[(df_2005["State"]=="CHANDIGARH")]
df_st_CH_2005
Out[886]:
In [887]:
t_CH_2005 = df_st_CH_2005.loc[:,"Total"]
s5_24 = np.sum(t_CH_2005)
s5_24
Out[887]:
In [888]:
df_st_CT_2005 = df_2005.loc[(df_2005["State"]=="CHHATTISGARH")]
df_st_CT_2005
Out[888]:
In [889]:
t_CT_2005 = df_st_CT_2005.loc[:,"Total"]
s5_25 = np.sum(t_CT_2005)
s5_25
Out[889]:
In [890]:
df_st_DN_2005 = df_2005.loc[(df_2005["State"]=="D & N HAVELI")]
df_st_DN_2005
Out[890]:
In [891]:
t_DN_2005 = df_st_DN_2005.loc[:,"Total"]
s5_26 = np.sum(t_DN_2005)
s5_26
Out[891]:
In [892]:
df_st_DD_2005 = df_2005.loc[(df_2005["State"]=="DAMAN & DIU")]
df_st_DD_2005
Out[892]:
In [893]:
t_DD_2005 = df_st_DD_2005.loc[:,"Total"]
s5_27 = np.sum(t_DD_2005)
s5_27
Out[893]:
In [894]:
df_st_DL_2005 = df_2005.loc[(df_2005["State"]=="DELHI (UT)")]
df_st_DL_2005
Out[894]:
In [895]:
t_DL_2005 = df_st_DL_2005.loc[:,"Total"]
s5_28 = np.sum(t_DL_2005)
s5_28
Out[895]:
In [896]:
df_st_GOA_2005 = df_2005.loc[(df_2005["State"]=="GOA")]
df_st_GOA_2005
Out[896]:
In [897]:
t_GOA_2005 = df_st_GOA_2005.loc[:,"Total"]
s5_29 = np.sum(t_GOA_2005)
s5_29
Out[897]:
In [898]:
df_st_GJ_2005 = df_2005.loc[(df_2005["State"]=="GUJARAT")]
df_st_GJ_2005
Out[898]:
In [899]:
t_GJ_2005 = df_st_GJ_2005.loc[:,"Total"]
s5_30 = np.sum(t_GJ_2005)
s5_30
Out[899]:
In [900]:
df_st_HR_2005 = df_2005.loc[(df_2005["State"]=="HARYANA")]
df_st_HR_2005
Out[900]:
In [901]:
t_HR_2005 = df_st_HR_2005.loc[:,"Total"]
s5_31 = np.sum(t_HR_2005)
s5_31
Out[901]:
In [902]:
df_st_HP_2005 = df_2005.loc[(df_2005["State"]=="HIMACHAL PRADESH")]
df_st_HP_2005
Out[902]:
In [903]:
t_HP_2005 = df_st_HP_2005.loc[:,"Total"]
s5_32 = np.sum(t_HP_2005)
s5_32
Out[903]:
In [904]:
df_st_JK_2005 = df_2005.loc[(df_2005["State"]=="JAMMU & KASHMIR")]
df_st_JK_2005
Out[904]:
In [905]:
t_JK_2005 = df_st_JK_2005.loc[:,"Total"]
s5_33 = np.sum(t_JK_2005)
s5_33
Out[905]:
In [906]:
df_st_JH_2005 = df_2005.loc[(df_2005["State"]=="JHARKHAND")]
df_st_JH_2005
Out[906]:
In [907]:
t_JH_2005 = df_st_JH_2005.loc[:,"Total"]
s5_34 = np.sum(t_JH_2005)
s5_34
Out[907]:
In [908]:
df_st_KN_2005 = df_2005.loc[(df_2005["State"]=="KARNATAKA")]
df_st_KN_2005
Out[908]:
In [909]:
t_KN_2005 = df_st_KN_2005.loc[:,"Total"]
s5_35 = np.sum(t_KN_2005)
s5_35
Out[909]:
In [910]:
df_st_KER_2005 = df_2005.loc[(df_2005["State"]=="KERALA")]
df_st_KER_2005
Out[910]:
In [911]:
t_KER_2005 = df_st_KER_2005.loc[:,"Total"]
s5_36 = np.sum(t_KER_2005)
s5_36
Out[911]:
In [912]:
df_st_LD_2005 = df_2005.loc[(df_2005["State"]=="LAKSHADWEEP")]
df_st_LD_2005
Out[912]:
In [913]:
t_LD_2005 = df_st_LD_2005.loc[:,"Total"]
s5_37 = np.sum(t_LD_2005)
s5_37
Out[913]:
In [914]:
df_st_MP_2005 = df_2005.loc[(df_2005["State"]=="MADHYA PRADESH")]
df_st_MP_2005
Out[914]:
In [915]:
t_MP_2005 = df_st_MP_2005.loc[:,"Total"]
s5_38 = np.sum(t_MP_2005)
s5_38
Out[915]:
In [916]:
df_st_MH_2005 = df_2005.loc[(df_2005["State"]=="MAHARASHTRA")]
df_st_MH_2005
Out[916]:
In [917]:
t_MH_2005 = df_st_MH_2005.loc[:,"Total"]
s5_39 = np.sum(t_MH_2005)
s5_39
Out[917]:
In [918]:
df_st_MN_2005 = df_2005.loc[(df_2005["State"]=="MANIPUR")]
df_st_MN_2005
Out[918]:
In [919]:
t_MN_2005 = df_st_MN_2005.loc[:,"Total"]
s5_40 = np.sum(t_MN_2005)
s5_40
Out[919]:
In [920]:
df_st_MG_2005 = df_2005.loc[(df_2005["State"]=="MEGHALAYA")]
df_st_MG_2005
Out[920]:
In [921]:
t_MG_2005 = df_st_MG_2005.loc[:,"Total"]
s5_41 = np.sum(t_MG_2005)
s5_41
Out[921]:
In [922]:
df_st_MZ_2005 = df_2005.loc[(df_2005["State"]=="MIZORAM")]
df_st_MZ_2005
Out[922]:
In [923]:
t_MN_2005 = df_st_MN_2005.loc[:,"Total"]
s5_42 = np.sum(t_MN_2005)
s5_42
Out[923]:
In [924]:
df_st_NG_2005 = df_2005.loc[(df_2005["State"]=="NAGALAND")]
df_st_NG_2005
Out[924]:
In [925]:
t_NG_2005 = df_st_NG_2005.loc[:,"Total"]
s5_43 = np.sum(t_NG_2005)
s5_43
Out[925]:
In [926]:
df_st_OD_2005 = df_2005.loc[(df_2005["State"]=="ODISHA")]
df_st_OD_2005
Out[926]:
In [927]:
t_OD_2005 = df_st_OD_2005.loc[:,"Total"]
s5_44 = np.sum(t_OD_2005)
s5_44
Out[927]:
In [928]:
df_st_PD_2005 = df_2005.loc[(df_2005["State"]=="PUDUCHERRY")]
df_st_PD_2005
Out[928]:
In [929]:
t_PD_2005 = df_st_PD_2005.loc[:,"Total"]
s5_45 = np.sum(t_PD_2005)
s5_45
Out[929]:
In [930]:
df_st_PB_2005 = df_2005.loc[(df_2005["State"]=="PUNJAB")]
df_st_PB_2005
Out[930]:
In [931]:
t_PB_2005 = df_st_PB_2005.loc[:,"Total"]
s5_46 = np.sum(t_PB_2005)
s5_46
Out[931]:
In [932]:
df_st_RJ_2005 = df_2005.loc[(df_2005["State"]=="RAJASTHAN")]
df_st_RJ_2005
Out[932]:
In [933]:
t_RJ_2005 = df_st_RJ_2005.loc[:,"Total"]
s5_47 = np.sum(t_RJ_2005)
s5_47
Out[933]:
In [934]:
df_st_SM_2005 = df_2005.loc[(df_2005["State"]=="SIKKIM")]
df_st_SM_2005
Out[934]:
In [935]:
t_SM_2005 = df_st_SM_2005.loc[:,"Total"]
s5_48 = np.sum(t_SM_2005)
s5_48
Out[935]:
In [936]:
df_st_TN_2005 = df_2005.loc[(df_2005["State"]=="TAMIL NADU")]
df_st_TN_2005
Out[936]:
In [937]:
t_TN_2005 = df_st_TN_2005.loc[:,"Total"]
s5_49 = np.sum(t_TN_2005)
s5_49
Out[937]:
In [938]:
df_st_TP_2005 = df_2005.loc[(df_2005["State"]=="TRIPURA")]
df_st_TP_2005
Out[938]:
In [939]:
t_TP_2005 = df_st_TP_2005.loc[:,"Total"]
s5_50 = np.sum(t_TP_2005)
s5_50
Out[939]:
In [940]:
df_st_UP_2005 = df_2005.loc[(df_2005["State"]=="UTTAR PRADESH")]
df_st_UP_2005
Out[940]:
In [941]:
t_UP_2005 = df_st_UP_2005.loc[:,"Total"]
s5_51 = np.sum(t_UP_2005)
s5_51
Out[941]:
In [942]:
df_st_UK_2005 = df_2005.loc[(df_2005["State"]=="UTTARAKHAND")]
df_st_UK_2005
Out[942]:
In [943]:
t_UK_2005 = df_st_UK_2005.loc[:,"Total"]
s5_52 = np.sum(t_UK_2005)
s5_52
Out[943]:
In [944]:
df_st_WB_2005 = df_2005.loc[(df_2005["State"]=="WEST BENGAL")]
df_st_WB_2005
Out[944]:
In [945]:
t_WB_2005 = df_st_WB_2005.loc[:,"Total"]
s5_53 = np.sum(t_WB_2005)
s5_53
Out[945]:
In [946]:
t_ANm_2005 = np.sum(df_st_AN_2005.where(df_st_AN_2005["Gender"]=="Male")["Total"])
t_ANm_2005
Out[946]:
In [947]:
t_ANfm_2005 = np.sum(df_st_AN_2005.where(df_st_AN_2005["Gender"]=="Female")["Total"])
t_ANfm_2005
Out[947]:
In [948]:
t_APm_2005 = np.sum(df_st_AP_2005.where(df_st_AP_2005["Gender"]=="Male")["Total"])
t_APm_2005
Out[948]:
In [949]:
t_APfm_2005 = np.sum(df_st_AP_2005.where(df_st_AP_2005["Gender"]=="Female")["Total"])
t_APfm_2005
Out[949]:
In [950]:
t_ARPm_2005 = np.sum(df_st_ARP_2005.where(df_st_ARP_2005["Gender"]=="Male")["Total"])
t_ARPm_2005
Out[950]:
In [951]:
t_ARPfm_2005 = np.sum(df_st_ARP_2005.where(df_st_ARP_2005["Gender"]=="Female")["Total"])
t_ARPfm_2005
Out[951]:
In [952]:
t_ASm_2005 = np.sum(df_st_AS_2005.where(df_st_AS_2005["Gender"]=="Male")["Total"])
t_ASm_2005
Out[952]:
In [953]:
t_ASfm_2005 = np.sum(df_st_AS_2005.where(df_st_AS_2005["Gender"]=="Female")["Total"])
t_ASfm_2005
Out[953]:
In [954]:
t_BHm_2005 = np.sum(df_st_BH_2005.where(df_st_BH_2005["Gender"]=="Male")["Total"])
t_BHm_2005
Out[954]:
In [955]:
t_BHfm_2005 = np.sum(df_st_BH_2005.where(df_st_BH_2005["Gender"]=="Female")["Total"])
t_BHfm_2005
Out[955]:
In [956]:
t_CHm_2005 = np.sum(df_st_CH_2005.where(df_st_CH_2005["Gender"]=="Male")["Total"])
t_CHm_2005
Out[956]:
In [957]:
t_CHfm_2005 = np.sum(df_st_CH_2005.where(df_st_CH_2005["Gender"]=="Female")["Total"])
t_CHfm_2005
Out[957]:
In [958]:
t_CTm_2005 = np.sum(df_st_CT_2005.where(df_st_CT_2005["Gender"]=="Male")["Total"])
t_CTm_2005
Out[958]:
In [959]:
t_CTfm_2005 = np.sum(df_st_CT_2005.where(df_st_CT_2005["Gender"]=="Female")["Total"])
t_CTfm_2005
Out[959]:
In [960]:
t_DNm_2005 = np.sum(df_st_DN_2005.where(df_st_DN_2005["Gender"]=="Male")["Total"])
t_DNm_2005
Out[960]:
In [961]:
t_DNfm_2005 = np.sum(df_st_DN_2005.where(df_st_DN_2005["Gender"]=="Female")["Total"])
t_DNfm_2005
Out[961]:
In [962]:
t_DDm_2005 = np.sum(df_st_DD_2005.where(df_st_DD_2005["Gender"]=="Male")["Total"])
t_DDm_2005
Out[962]:
In [963]:
t_DDfm_2005 = np.sum(df_st_DD_2005.where(df_st_DD_2005["Gender"]=="Female")["Total"])
t_DDfm_2005
Out[963]:
In [964]:
t_DLm_2005 = np.sum(df_st_DL_2005.where(df_st_DL_2005["Gender"]=="Male")["Total"])
t_DLm_2005
Out[964]:
In [965]:
t_DLfm_2005 = np.sum(df_st_DL_2005.where(df_st_DL_2005["Gender"]=="Female")["Total"])
t_DLfm_2005
Out[965]:
In [966]:
t_GOAm_2005 = np.sum(df_st_GOA_2005.where(df_st_GOA_2005["Gender"]=="Male")["Total"])
t_GOAm_2005
Out[966]:
In [967]:
t_GOAfm_2005 = np.sum(df_st_GOA_2005.where(df_st_GOA_2005["Gender"]=="Female")["Total"])
t_GOAfm_2005
Out[967]:
In [968]:
t_GJm_2005 = np.sum(df_st_GJ_2005.where(df_st_GJ_2005["Gender"]=="Male")["Total"])
t_GJm_2005
Out[968]:
In [969]:
t_GJfm_2005 = np.sum(df_st_GJ_2005.where(df_st_GJ_2005["Gender"]=="Female")["Total"])
t_GJfm_2005
Out[969]:
In [970]:
t_HRm_2005 = np.sum(df_st_HR_2005.where(df_st_HR_2005["Gender"]=="Male")["Total"])
t_HRm_2005
Out[970]:
In [971]:
t_HRfm_2005 = np.sum(df_st_HR_2005.where(df_st_HR_2005["Gender"]=="Female")["Total"])
t_HRfm_2005
Out[971]:
In [972]:
t_HPm_2005 = np.sum(df_st_HP_2005.where(df_st_HP_2005["Gender"]=="Male")["Total"])
t_HPm_2005
Out[972]:
In [973]:
t_HPfm_2005 = np.sum(df_st_HP_2005.where(df_st_HP_2005["Gender"]=="Female")["Total"])
t_HPfm_2005
Out[973]:
In [974]:
t_JKm_2005 = np.sum(df_st_JK_2005.where(df_st_JK_2005["Gender"]=="Male")["Total"])
t_JKm_2005
Out[974]:
In [975]:
t_JKfm_2005 = np.sum(df_st_JK_2005.where(df_st_JK_2005["Gender"]=="Female")["Total"])
t_JKfm_2005
Out[975]:
In [976]:
t_JHm_2005 = np.sum(df_st_JH_2005.where(df_st_JH_2005["Gender"]=="Male")["Total"])
t_JHm_2005
Out[976]:
In [977]:
t_JHfm_2005 = np.sum(df_st_JH_2005.where(df_st_JH_2005["Gender"]=="Female")["Total"])
t_JHfm_2005
Out[977]:
In [978]:
t_KNm_2005 = np.sum(df_st_KN_2005.where(df_st_KN_2005["Gender"]=="Male")["Total"])
t_KNm_2005
Out[978]:
In [979]:
t_KNfm_2005 = np.sum(df_st_KN_2005.where(df_st_KN_2005["Gender"]=="Female")["Total"])
t_KNfm_2005
Out[979]:
In [980]:
t_KERm_2005 = np.sum(df_st_KER_2005.where(df_st_KER_2005["Gender"]=="Male")["Total"])
t_KERm_2005
Out[980]:
In [981]:
t_KERfm_2005 = np.sum(df_st_KER_2005.where(df_st_KER_2005["Gender"]=="Female")["Total"])
t_KERfm_2005
Out[981]:
In [982]:
t_LDm_2005 = np.sum(df_st_LD_2005.where(df_st_LD_2005["Gender"]=="Male")["Total"])
t_LDm_2005
Out[982]:
In [983]:
t_LDfm_2005 = np.sum(df_st_LD_2005.where(df_st_LD_2005["Gender"]=="Female")["Total"])
t_LDfm_2005
Out[983]:
In [984]:
t_MPm_2005 = np.sum(df_st_MP_2005.where(df_st_MP_2005["Gender"]=="Male")["Total"])
t_MPm_2005
Out[984]:
In [985]:
t_MPfm_2005 = np.sum(df_st_MP_2005.where(df_st_MP_2005["Gender"]=="Female")["Total"])
t_MPfm_2005
Out[985]:
In [986]:
t_MHm_2005 = np.sum(df_st_MH_2005.where(df_st_MH_2005["Gender"]=="Male")["Total"])
t_MHm_2005
Out[986]:
In [987]:
t_MHfm_2005 = np.sum(df_st_MH_2005.where(df_st_MH_2005["Gender"]=="Female")["Total"])
t_MHfm_2005
Out[987]:
In [988]:
t_MNm_2005 = np.sum(df_st_MN_2005.where(df_st_MN_2005["Gender"]=="Male")["Total"])
t_MNm_2005
Out[988]:
In [989]:
t_MNfm_2005 = np.sum(df_st_MN_2005.where(df_st_MN_2005["Gender"]=="Female")["Total"])
t_MNfm_2005
Out[989]:
In [990]:
t_MGm_2005 = np.sum(df_st_MG_2005.where(df_st_MG_2005["Gender"]=="Male")["Total"])
t_MGm_2005
Out[990]:
In [991]:
t_MGfm_2005 = np.sum(df_st_MG_2005.where(df_st_MG_2005["Gender"]=="Female")["Total"])
t_MGfm_2005
Out[991]:
In [992]:
t_MZm_2005 = np.sum(df_st_MZ_2005.where(df_st_MZ_2005["Gender"]=="Male")["Total"])
t_MZm_2005
Out[992]:
In [993]:
t_MZfm_2005 = np.sum(df_st_MZ_2005.where(df_st_MZ_2005["Gender"]=="Female")["Total"])
t_MZfm_2005
Out[993]:
In [994]:
t_NGm_2005 = np.sum(df_st_NG_2005.where(df_st_NG_2005["Gender"]=="Male")["Total"])
t_NGm_2005
Out[994]:
In [995]:
t_NGfm_2005 = np.sum(df_st_NG_2005.where(df_st_NG_2005["Gender"]=="Female")["Total"])
t_MGfm_2005
Out[995]:
In [996]:
t_ODm_2005 = np.sum(df_st_OD_2005.where(df_st_OD_2005["Gender"]=="Male")["Total"])
t_ODm_2005
Out[996]:
In [997]:
t_ODfm_2005 = np.sum(df_st_OD_2005.where(df_st_OD_2005["Gender"]=="Female")["Total"])
t_ODfm_2005
Out[997]:
In [998]:
t_PDm_2005 = np.sum(df_st_PD_2005.where(df_st_PD_2005["Gender"]=="Male")["Total"])
t_PDm_2005
Out[998]:
In [999]:
t_PDfm_2005 = np.sum(df_st_PD_2005.where(df_st_PD_2005["Gender"]=="Female")["Total"])
t_PDfm_2005
Out[999]:
In [1000]:
t_PBm_2005 = np.sum(df_st_PB_2005.where(df_st_PB_2005["Gender"]=="Male")["Total"])
t_PBm_2005
Out[1000]:
In [1001]:
t_PBfm_2005 = np.sum(df_st_PB_2005.where(df_st_PB_2005["Gender"]=="Female")["Total"])
t_PBfm_2005
Out[1001]:
In [1002]:
t_RJm_2005 = np.sum(df_st_RJ_2005.where(df_st_RJ_2005["Gender"]=="Male")["Total"])
t_RJm_2005
Out[1002]:
In [1003]:
t_RJfm_2005 = np.sum(df_st_RJ_2005.where(df_st_RJ_2005["Gender"]=="Female")["Total"])
t_RJfm_2005
Out[1003]:
In [1004]:
t_SMm_2005 = np.sum(df_st_SM_2005.where(df_st_SM_2005["Gender"]=="Male")["Total"])
t_SMm_2005
Out[1004]:
In [1005]:
t_SMfm_2005 = np.sum(df_st_SM_2005.where(df_st_SM_2005["Gender"]=="Female")["Total"])
t_SMfm_2005
Out[1005]:
In [1006]:
t_TNm_2005 = np.sum(df_st_TN_2005.where(df_st_TN_2005["Gender"]=="Male")["Total"])
t_TNm_2005
Out[1006]:
In [1007]:
t_TNfm_2005 = np.sum(df_st_TN_2005.where(df_st_TN_2005["Gender"]=="Female")["Total"])
t_TNfm_2005
Out[1007]:
In [1008]:
t_TPm_2005 = np.sum(df_st_TP_2005.where(df_st_TP_2005["Gender"]=="Male")["Total"])
t_TPm_2005
Out[1008]:
In [1009]:
t_TPfm_2005 = np.sum(df_st_TP_2005.where(df_st_TP_2005["Gender"]=="Female")["Total"])
t_TPfm_2005
Out[1009]:
In [1010]:
t_UPm_2005 = np.sum(df_st_UP_2005.where(df_st_UP_2005["Gender"]=="Male")["Total"])
t_UPm_2005
Out[1010]:
In [1011]:
t_UPfm_2005 = np.sum(df_st_UP_2005.where(df_st_UP_2005["Gender"]=="Female")["Total"])
t_UPfm_2005
Out[1011]:
In [1012]:
t_UKm_2005 = np.sum(df_st_UK_2005.where(df_st_UK_2005["Gender"]=="Male")["Total"])
t_UKm_2005
Out[1012]:
In [1013]:
t_UKfm_2005 = np.sum(df_st_UK_2005.where(df_st_UK_2005["Gender"]=="Female")["Total"])
t_UKfm_2005
Out[1013]:
In [1014]:
t_WBm_2005 = np.sum(df_st_WB_2005.where(df_st_WB_2005["Gender"]=="Male")["Total"])
t_WBm_2005
Out[1014]:
In [1015]:
t_WBfm_2005 = np.sum(df_st_WB_2005.where(df_st_WB_2005["Gender"]=="Female")["Total"])
t_WBfm_2005
Out[1015]:
In [1016]:
l_2005=list(df_2005.loc[:,"Type"].unique())
l_2005.sort()
print(l_2005,end=" ")
In [1017]:
cause_l_2005=[]
for i in l_2005:
rough= np.sum(df_2005.where(df_2005["Type"]==i)["Total"])
cause_l_2005.append(rough)
print(cause_l_2005,end=" ")
In [1018]:
x1_2005 = set(df_2005.loc[:,"State"])
x1_2005
len(x1_2005)
Out[1018]:
In [1019]:
cause_df_2005 = pd.DataFrame(cause_l_2005,l_2005)
cause_df_2005.reset_index()
Out[1019]:
In [1020]:
cause_df_2005.plot.bar(stacked=True,figsize=(50,20))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("Causes",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths due to Different Causes in 2005",fontsize=55)
plt.legend(["Death Count"], prop={"size":30})
Out[1020]:
In [1021]:
s_2005=list(df_2005.loc[:,"State"].unique())
s_2005.sort()
print(s_2005,end=" ")
In [1022]:
state_s_2005 = [s5_19,s5_20,s5_21,s5_22,s5_23,s5_24,s5_25,s5_26,s5_27,s5_28,s5_29,s5_30,s5_31,s5_32,s5_33,s5_34,
s5_35,s5_36,s5_37,s5_38,s5_39,s5_40,s5_41,s5_42,s5_43,s5_44,s5_45,s5_46,s5_47,s5_48,s5_49,s5_50,
s5_51,s5_52,s5_53]
print(state_s_2005,end=" ")
In [1023]:
state_m_2005=[t_ANm_2005, t_APm_2005, t_ARPm_2005, t_ASm_2005, t_BHm_2005, t_CHm_2005, t_CTm_2005, t_DNm_2005,
t_DDm_2005, t_DLm_2005, t_GOAm_2005, t_GJm_2005, t_HRm_2005, t_HPm_2005,t_JKm_2005, t_JHm_2005,
t_KNm_2005, t_KERm_2005, t_LDm_2005,t_MPm_2005,t_MHm_2005,t_MNm_2005,t_MGm_2005,t_MZm_2005,
t_NGm_2005, t_ODm_2005, t_PDm_2005,t_PBm_2005,t_RJm_2005,t_SMm_2005,t_TNm_2005,t_TPm_2005,
t_UPm_2005, t_UKm_2005, t_WBm_2005]
print(state_m_2005,end=" ")
In [1024]:
state_fm_2005=[t_ANfm_2005, t_APfm_2005, t_ARPfm_2005, t_ASfm_2005, t_BHfm_2005, t_CHfm_2005, t_CTfm_2005, t_DNfm_2005,
t_DDfm_2005, t_DLfm_2005, t_GOAfm_2005, t_GJfm_2005, t_HRfm_2005, t_HPfm_2005,t_JKfm_2005, t_JHfm_2005,
t_KNfm_2005, t_KERfm_2005, t_LDfm_2005,t_MPfm_2005,t_MHfm_2005,t_MNfm_2005,t_MGfm_2005,t_MZfm_2005,
t_NGfm_2005, t_ODfm_2005, t_PDfm_2005,t_PBfm_2005,t_RJfm_2005,t_SMfm_2005,t_TNfm_2005,t_TPfm_2005,
t_UPfm_2005, t_UKfm_2005, t_WBfm_2005]
print(state_fm_2005,end=" ")
In [1025]:
df_state_2005 = {
"State":['A & N ISLANDS', 'ANDHRA PRADESH', 'ARUNACHAL PRADESH', 'ASSAM', 'BIHAR', 'CHANDIGARH', 'CHHATTISGARH',
'D & N HAVELI', 'DAMAN & DIU', 'DELHI (UT)', 'GOA', 'GUJARAT', 'HARYANA', 'HIMACHAL PRADESH', 'JAMMU & KASHMIR',
'JHARKHAND', 'KARNATAKA', 'KERALA', 'LAKSHADWEEP', 'MADHYA PRADESH', 'MAHARASHTRA', 'MANIPUR', 'MEGHALAYA',
'MIZORAM', 'NAGALAND', 'ODISHA', 'PUDUCHERRY', 'PUNJAB', 'RAJASTHAN', 'SIKKIM', 'TAMIL NADU', 'TRIPURA',
'UTTAR PRADESH', 'UTTARAKHAND', 'WEST BENGAL'],
"Total":[s5_19,s5_20,s5_21,s5_22,s5_23,s5_24,s5_25,s5_26,s5_27,s5_28,s5_29,s5_30,s5_31,s5_32,s5_33,s5_34,
s5_35,s5_36,s5_37,s5_38,s5_39,s5_40,s5_41,s5_42,s5_43,s5_44,s5_45,s5_46,s5_47,s5_48,s5_49,s5_50,
s5_51,s5_52,s5_53],
"Male":[t_ANm_2005, t_APm_2005, t_ARPm_2005, t_ASm_2005, t_BHm_2005, t_CHm_2005, t_CTm_2005, t_DNm_2005,
t_DDm_2005, t_DLm_2005, t_GOAm_2005, t_GJm_2005, t_HRm_2005, t_HPm_2005,t_JKm_2005, t_JHm_2005,
t_KNm_2005, t_KERm_2005, t_LDm_2005,t_MPm_2005,t_MHm_2005,t_MNm_2005,t_MGm_2005,t_MZm_2005,
t_NGm_2005, t_ODm_2005, t_PDm_2005,t_PBm_2005,t_RJm_2005,t_SMm_2005,t_TNm_2005,t_TPm_2005,
t_UPm_2005, t_UKm_2005, t_WBm_2005],
"Female":[t_ANfm_2005, t_APfm_2005, t_ARPfm_2005, t_ASfm_2005, t_BHfm_2005, t_CHfm_2005, t_CTfm_2005, t_DNfm_2005,
t_DDfm_2005, t_DLfm_2005, t_GOAfm_2005, t_GJfm_2005, t_HRfm_2005, t_HPfm_2005,t_JKfm_2005, t_JHfm_2005,
t_KNfm_2005, t_KERfm_2005, t_LDfm_2005,t_MPfm_2005,t_MHfm_2005,t_MNfm_2005,t_MGfm_2005,t_MZfm_2005,
t_NGfm_2005, t_ODfm_2005, t_PDfm_2005,t_PBfm_2005,t_RJfm_2005,t_SMfm_2005,t_TNfm_2005,t_TPfm_2005,
t_UPfm_2005, t_UKfm_2005, t_WBfm_2005]
}
df_All_st_2005 = pd.DataFrame(df_state_2005)
df_All_st_2005
Out[1025]:
In [1026]:
pivot_2005 = pd.pivot_table(df_All_st_2005, values=None, index='State', columns=None,
fill_value=None, margins=False, dropna=True, margins_name='All', observed=False)
pivot_2005
Out[1026]:
In [1027]:
pivot_2005.plot(kind='bar',figsize=(60,30))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("States",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths of Males And Females In Different State",fontsize=55)
plt.legend(["Female","Male","Total"], prop={"size":30})
plt.show
Out[1027]:
In [1028]:
cause_l_percent_2005=[]
for i in cause_l_2005:
p=i/np.sum(cause_l_2005)
avg_2005_cause=p*100
cause_l_percent_2005.append(avg_2005_cause)
print(cause_l_percent_2005,end=" ")
In [1029]:
np.max(cause_l_percent_2005) #Married
Out[1029]:
In [1030]:
np.min(cause_l_percent_2005) #Ideological Causes/Hero Worshipping
Out[1030]:
In [1031]:
state_s_2005_percent=[]
tot=np.sum(state_s_2005)
for i in state_s_2005:
xy=i/tot
avg_state_2005=xy*100
state_s_2005_percent.append(avg_state_2005)
print(state_s_2005_percent,end=" ")
In [1032]:
np.max(state_s_2005_percent) #Maharashtra
Out[1032]:
In [1033]:
np.min(state_s_2005_percent) #Lakshdweep
Out[1033]:
In [1034]:
l_age_grp_2005=[s5_4,s5_5,s5_6,s5_7,s5_8,s5_54]
l_age_grp_2005
Out[1034]:
In [1035]:
l_age_grp_2005_percent=[]
for i in l_age_grp_2005:
yz=i/s5_1
avg_2005_age_grp=yz*100
l_age_grp_2005_percent.append(avg_2005_age_grp)
l_age_grp_2005_percent
Out[1035]:
In [1036]:
np.max(l_age_grp_2005_percent) #0-100+
Out[1036]:
In [1037]:
np.min(l_age_grp_2005_percent) #0-14
Out[1037]:
In [1038]:
total_male_percent_2005= (s5_2/s5_1)*100
total_male_percent_2005
Out[1038]:
In [1039]:
total_female_percent_2005= (s5_3/s5_1)*100
total_female_percent_2005
Out[1039]:
In [ ]:
2006¶
In [1040]:
df_2006 = suicide_df.loc[(suicide_df["Year"]==2006)]
df_2006
Out[1040]:
In [1041]:
t2006_dcount = df_2006.loc[:,"Total"]
s6_1 = np.sum(t2006_dcount)
s6_1
Out[1041]:
In [1042]:
male_2006 = df_2006.loc[(df_2006["Gender"]=="Male")]
male_2006
Out[1042]:
In [1043]:
m2006_count = male_2006.loc[:,"Total"]
s6_2 = np.sum(m2006_count)
s6_2
Out[1043]:
In [1044]:
female_2006 = df_2006.loc[(df_2006["Gender"]=="Female")]
female_2006
Out[1044]:
In [1045]:
fm2006_count = female_2006.loc[:,"Total"]
s6_3 = np.sum(fm2006_count)
s6_3
Out[1045]:
In [1046]:
df_age_d014_2006 = df_2006.loc[(df_2006["Age_group"]=="0-14")]
df_age_d014_2006
Out[1046]:
In [1047]:
t_014_2006 = df_age_d014_2006.loc[:,"Total"]
s6_4 = np.sum(t_014_2006)
s6_4
Out[1047]:
In [1048]:
df_age_d1529_2006 = df_2006.loc[(df_2006["Age_group"]=="15-29")]
df_age_d1529_2006
Out[1048]:
In [1049]:
t_1529_2006 = df_age_d1529_2006.loc[:,"Total"]
s6_5 = np.sum(t_1529_2006)
s6_5
Out[1049]:
In [1050]:
df_age_d3044_2006 = df_2006.loc[(df_2006["Age_group"]=="30-44")]
df_age_d3044_2006
Out[1050]:
In [1051]:
t_3044_2006 = df_age_d3044_2006.loc[:,"Total"]
s6_6 = np.sum(t_3044_2006)
s6_6
Out[1051]:
In [1052]:
df_age_d4559_2006 = df_2006.loc[(df_2006["Age_group"]=="45-59")]
df_age_d4559_2006
Out[1052]:
In [1053]:
t_4559_2006 = df_age_d4559_2006.loc[:,"Total"]
s6_7 = np.sum(t_4559_2006)
s6_7
Out[1053]:
In [1054]:
df_age_d60_2006 = df_2006.loc[(df_2006["Age_group"]=="60+")]
df_age_d60_2006
Out[1054]:
In [1055]:
t_60_2006 = df_age_d60_2006.loc[:,"Total"]
s6_8 = np.sum(t_60_2006)
s6_8
Out[1055]:
In [1056]:
df_age_d0100_2006 = df_2006.loc[(df_2006["Age_group"]=="0-100+")]
df_age_d0100_2006
Out[1056]:
In [1057]:
t_0100_2006 = df_age_d0100_2006.loc[:,"Total"]
s6_54 = np.sum(t_0100_2006)
s6_54
Out[1057]:
In [1058]:
df_014m_2006 = df_age_d014_2006.loc[(df_age_d014_2006["Gender"]=="Male")]
df_014m_2006
Out[1058]:
In [1059]:
t_014m_2006 = df_014m_2006.loc[:,"Total"]
s6_9 = np.sum(t_014m_2006)
s6_9
Out[1059]:
In [1060]:
df_014fm_2006 = df_age_d014_2006.loc[(df_age_d014_2006["Gender"]=="Female")]
df_014fm_2006
Out[1060]:
In [1061]:
t_014fm_2006 = df_014fm_2006.loc[:,"Total"]
s6_10 = np.sum(t_014fm_2006)
s6_10
Out[1061]:
In [1062]:
df_1529m_2006 = df_age_d1529_2006.loc[(df_age_d1529_2006["Gender"]=="Male")]
df_1529m_2006
Out[1062]:
In [1063]:
t_1529m_2006 = df_1529m_2006.loc[:,"Total"]
s6_11 = np.sum(t_1529m_2006)
s6_11
Out[1063]:
In [1064]:
df_1529fm_2006 = df_age_d1529_2006.loc[(df_age_d1529_2006["Gender"]=="Female")]
df_1529fm_2006
Out[1064]:
In [1065]:
t_1529fm_2006 = df_1529fm_2006.loc[:,"Total"]
s6_12 = np.sum(t_1529fm_2006)
s6_12
Out[1065]:
In [1066]:
df_3044m_2006 = df_age_d3044_2006.loc[(df_age_d3044_2006["Gender"]=="Male")]
df_3044m_2006
Out[1066]:
In [1067]:
t_3044m_2006 = df_3044m_2006.loc[:,"Total"]
s6_13 = np.sum(t_3044m_2006)
s6_13
Out[1067]:
In [1068]:
df_3044fm_2006 = df_age_d3044_2006.loc[(df_age_d3044_2006["Gender"]=="Female")]
df_3044fm_2006
Out[1068]:
In [1069]:
t_3044fm_2006 = df_3044fm_2006.loc[:,"Total"]
s6_14 = np.sum(t_3044fm_2006)
s6_14
Out[1069]:
In [1070]:
df_4559m_2006 = df_age_d4559_2006.loc[(df_age_d4559_2006["Gender"]=="Male")]
df_4559m_2006
Out[1070]:
In [1071]:
t_4559m_2006 = df_4559m_2006.loc[:,"Total"]
s6_15 = np.sum(t_4559m_2006)
s6_15
Out[1071]:
In [1072]:
df_4559fm_2006 = df_age_d4559_2006.loc[(df_age_d4559_2006["Gender"]=="Female")]
df_4559fm_2006
Out[1072]:
In [1073]:
t_4559fm_2006 = df_4559fm_2006.loc[:,"Total"]
s6_16 = np.sum(t_4559fm_2006)
s6_16
Out[1073]:
In [1074]:
df_60m_2006 = df_age_d60_2006.loc[(df_age_d60_2006["Gender"]=="Male")]
df_60m_2006
Out[1074]:
In [1075]:
t_60m_2006 = df_60m_2006.loc[:,"Total"]
s6_17 = np.sum(t_60m_2006)
s6_17
Out[1075]:
In [1076]:
df_60fm_2006 = df_age_d60_2006.loc[(df_age_d60_2006["Gender"]=="Female")]
df_60fm_2006
Out[1076]:
In [1077]:
t_60fm_2006 = df_60fm_2006.loc[:,"Total"]
s6_18 = np.sum(t_60fm_2006)
s6_18
Out[1077]:
In [1078]:
df_0100fm_2006 = df_age_d0100_2006.loc[(df_age_d0100_2006["Gender"]=="Female")]
df_0100fm_2006
Out[1078]:
In [1079]:
t_0100fm_2006 = df_0100fm_2006.loc[:,"Total"]
s6_55 = np.sum(t_0100fm_2006)
s6_55
Out[1079]:
In [1080]:
df_0100m_2006 = df_age_d0100_2006.loc[(df_age_d0100_2006["Gender"]=="Male")]
df_0100m_2006
Out[1080]:
In [1081]:
t_0100m_2006 = df_0100m_2006.loc[:,"Total"]
s6_56 = np.sum(t_0100m_2006)
s6_56
Out[1081]:
In [1082]:
df_st_AN_2006 = df_2006.loc[(df_2006["State"]=="A & N ISLANDS")]
df_st_AN_2006
Out[1082]:
In [1083]:
t_AN_2006 = df_st_AN_2006.loc[:,"Total"]
s6_19 = np.sum(t_AN_2006)
s6_19
Out[1083]:
In [1084]:
df_st_AP_2006 = df_2006.loc[(df_2006["State"]=="ANDHRA PRADESH")]
df_st_AP_2006
Out[1084]:
In [1085]:
t_AP_2006 = df_st_AP_2006.loc[:,"Total"]
s6_20 = np.sum(t_AP_2006)
s6_20
Out[1085]:
In [1086]:
df_st_ARP_2006 = df_2006.loc[(df_2006["State"]=="ARUNACHAL PRADESH")]
df_st_ARP_2006
Out[1086]:
In [1087]:
t_ARP_2006 = df_st_ARP_2006.loc[:,"Total"]
s6_21 = np.sum(t_ARP_2006)
s6_21
Out[1087]:
In [1088]:
df_st_AS_2006 = df_2006.loc[(df_2006["State"]=="ASSAM")]
df_st_AS_2006
Out[1088]:
In [1089]:
t_AS_2006 = df_st_AS_2006.loc[:,"Total"]
s6_22 = np.sum(t_AS_2006)
s6_22
Out[1089]:
In [1090]:
df_st_BH_2006 = df_2006.loc[(df_2006["State"]=="BIHAR")]
df_st_BH_2006
Out[1090]:
In [1091]:
t_BH_2006 = df_st_BH_2006.loc[:,"Total"]
s6_23 = np.sum(t_BH_2006)
s6_23
Out[1091]:
In [1092]:
df_st_CH_2006 = df_2006.loc[(df_2006["State"]=="CHANDIGARH")]
df_st_CH_2006
Out[1092]:
In [1093]:
t_CH_2006 = df_st_CH_2006.loc[:,"Total"]
s6_24 = np.sum(t_CH_2006)
s6_24
Out[1093]:
In [1094]:
df_st_CT_2006 = df_2006.loc[(df_2006["State"]=="CHHATTISGARH")]
df_st_CT_2006
Out[1094]:
In [1095]:
t_CT_2006 = df_st_CT_2006.loc[:,"Total"]
s6_25 = np.sum(t_CT_2006)
s6_25
Out[1095]:
In [1096]:
df_st_DN_2006 = df_2006.loc[(df_2006["State"]=="D & N HAVELI")]
df_st_DN_2006
Out[1096]:
In [1097]:
t_DN_2006 = df_st_DN_2006.loc[:,"Total"]
s6_26 = np.sum(t_DN_2006)
s6_26
Out[1097]:
In [1098]:
df_st_DD_2006 = df_2006.loc[(df_2006["State"]=="DAMAN & DIU")]
df_st_DD_2006
Out[1098]:
In [1099]:
t_DD_2006 = df_st_DD_2006.loc[:,"Total"]
s6_27 = np.sum(t_DD_2006)
s6_27
Out[1099]:
In [1100]:
df_st_DL_2006 = df_2006.loc[(df_2006["State"]=="DELHI (UT)")]
df_st_DL_2006
Out[1100]:
In [1101]:
t_DL_2006 = df_st_DL_2006.loc[:,"Total"]
s6_28 = np.sum(t_DL_2006)
s6_28
Out[1101]:
In [1102]:
df_st_GOA_2006 = df_2006.loc[(df_2006["State"]=="GOA")]
df_st_GOA_2006
Out[1102]:
In [1103]:
t_GOA_2006 = df_st_GOA_2006.loc[:,"Total"]
s6_29 = np.sum(t_GOA_2006)
s6_29
Out[1103]:
In [1104]:
df_st_GJ_2006 = df_2006.loc[(df_2006["State"]=="GUJARAT")]
df_st_GJ_2006
Out[1104]:
In [1105]:
t_GJ_2006 = df_st_GJ_2006.loc[:,"Total"]
s6_30 = np.sum(t_GJ_2006)
s6_30
Out[1105]:
In [1106]:
df_st_HR_2006 = df_2006.loc[(df_2006["State"]=="HARYANA")]
df_st_HR_2006
Out[1106]:
In [1107]:
t_HR_2006 = df_st_HR_2006.loc[:,"Total"]
s6_31 = np.sum(t_HR_2006)
s6_31
Out[1107]:
In [1108]:
df_st_HP_2006 = df_2006.loc[(df_2006["State"]=="HIMACHAL PRADESH")]
df_st_HP_2006
Out[1108]:
In [1109]:
t_HP_2006 = df_st_HP_2006.loc[:,"Total"]
s6_32 = np.sum(t_HP_2006)
s6_32
Out[1109]:
In [1110]:
df_st_JK_2006 = df_2006.loc[(df_2006["State"]=="JAMMU & KASHMIR")]
df_st_JK_2006
Out[1110]:
In [1111]:
t_JK_2006 = df_st_JK_2006.loc[:,"Total"]
s6_33 = np.sum(t_JK_2006)
s6_33
Out[1111]:
In [1112]:
df_st_JH_2006 = df_2006.loc[(df_2006["State"]=="JHARKHAND")]
df_st_JH_2006
Out[1112]:
In [1113]:
t_JH_2006 = df_st_JH_2006.loc[:,"Total"]
s6_34 = np.sum(t_JH_2006)
s6_34
Out[1113]:
In [1114]:
df_st_KN_2006 = df_2006.loc[(df_2006["State"]=="KARNATAKA")]
df_st_KN_2006
Out[1114]:
In [1115]:
t_KN_2006 = df_st_KN_2006.loc[:,"Total"]
s6_35 = np.sum(t_KN_2006)
s6_35
Out[1115]:
In [1116]:
df_st_KER_2006 = df_2006.loc[(df_2006["State"]=="KERALA")]
df_st_KER_2006
Out[1116]:
In [1117]:
t_KER_2006 = df_st_KER_2006.loc[:,"Total"]
s6_36 = np.sum(t_KER_2006)
s6_36
Out[1117]:
In [1118]:
df_st_LD_2006 = df_2006.loc[(df_2006["State"]=="LAKSHADWEEP")]
df_st_LD_2006
Out[1118]:
In [1119]:
t_LD_2006 = df_st_LD_2006.loc[:,"Total"]
s6_37 = np.sum(t_LD_2006)
s6_37
Out[1119]:
In [1120]:
df_st_MP_2006 = df_2006.loc[(df_2006["State"]=="MADHYA PRADESH")]
df_st_MP_2006
Out[1120]:
In [1121]:
t_MP_2006 = df_st_MP_2006.loc[:,"Total"]
s6_38 = np.sum(t_MP_2006)
s6_38
Out[1121]:
In [1122]:
df_st_MH_2006 = df_2006.loc[(df_2006["State"]=="MAHARASHTRA")]
df_st_MH_2006
Out[1122]:
In [1123]:
t_MH_2006 = df_st_MH_2006.loc[:,"Total"]
s6_39 = np.sum(t_MH_2006)
s6_39
Out[1123]:
In [1124]:
df_st_MN_2006 = df_2006.loc[(df_2006["State"]=="MANIPUR")]
df_st_MN_2006
Out[1124]:
In [1125]:
t_MN_2006 = df_st_MN_2006.loc[:,"Total"]
s6_40 = np.sum(t_MN_2006)
s6_40
Out[1125]:
In [1126]:
df_st_MG_2006 = df_2006.loc[(df_2006["State"]=="MEGHALAYA")]
df_st_MG_2006
Out[1126]:
In [1127]:
t_MG_2006 = df_st_MG_2006.loc[:,"Total"]
s6_41 = np.sum(t_MG_2006)
s6_41
Out[1127]:
In [1128]:
df_st_MZ_2006 = df_2006.loc[(df_2006["State"]=="MIZORAM")]
df_st_MZ_2006
Out[1128]:
In [1129]:
t_MN_2006 = df_st_MN_2006.loc[:,"Total"]
s6_42 = np.sum(t_MN_2006)
s6_42
Out[1129]:
In [1130]:
df_st_NG_2006 = df_2006.loc[(df_2006["State"]=="NAGALAND")]
df_st_NG_2006
Out[1130]:
In [1131]:
t_NG_2006 = df_st_NG_2006.loc[:,"Total"]
s6_43 = np.sum(t_NG_2006)
s6_43
Out[1131]:
In [1132]:
df_st_OD_2006 = df_2006.loc[(df_2006["State"]=="ODISHA")]
df_st_OD_2006
Out[1132]:
In [1133]:
t_OD_2006 = df_st_OD_2006.loc[:,"Total"]
s6_44 = np.sum(t_OD_2006)
s6_44
Out[1133]:
In [1134]:
df_st_PD_2006 = df_2006.loc[(df_2006["State"]=="PUDUCHERRY")]
df_st_PD_2006
Out[1134]:
In [1135]:
t_PD_2006 = df_st_PD_2006.loc[:,"Total"]
s6_45 = np.sum(t_PD_2006)
s6_45
Out[1135]:
In [1136]:
df_st_PB_2006 = df_2006.loc[(df_2006["State"]=="PUNJAB")]
df_st_PB_2006
Out[1136]:
In [1137]:
t_PB_2006 = df_st_PB_2006.loc[:,"Total"]
s6_46 = np.sum(t_PB_2006)
s6_46
Out[1137]:
In [1138]:
df_st_RJ_2006 = df_2006.loc[(df_2006["State"]=="RAJASTHAN")]
df_st_RJ_2006
Out[1138]:
In [1139]:
t_RJ_2006 = df_st_RJ_2006.loc[:,"Total"]
s6_47 = np.sum(t_RJ_2006)
s6_47
Out[1139]:
In [1140]:
df_st_SM_2006 = df_2006.loc[(df_2006["State"]=="SIKKIM")]
df_st_SM_2006
Out[1140]:
In [1141]:
t_SM_2006 = df_st_SM_2006.loc[:,"Total"]
s6_48 = np.sum(t_SM_2006)
s6_48
Out[1141]:
In [1142]:
df_st_TN_2006 = df_2006.loc[(df_2006["State"]=="TAMIL NADU")]
df_st_TN_2006
Out[1142]:
In [1143]:
t_TN_2006 = df_st_TN_2006.loc[:,"Total"]
s6_49 = np.sum(t_TN_2006)
s6_49
Out[1143]:
In [1144]:
df_st_TP_2006 = df_2006.loc[(df_2006["State"]=="TRIPURA")]
df_st_TP_2006
Out[1144]:
In [1145]:
t_TP_2006 = df_st_TP_2006.loc[:,"Total"]
s6_50 = np.sum(t_TP_2006)
s6_50
Out[1145]:
In [1146]:
df_st_UP_2006 = df_2006.loc[(df_2006["State"]=="UTTAR PRADESH")]
df_st_UP_2006
Out[1146]:
In [1147]:
t_UP_2006 = df_st_UP_2006.loc[:,"Total"]
s6_51 = np.sum(t_UP_2006)
s6_51
Out[1147]:
In [1148]:
df_st_UK_2006 = df_2006.loc[(df_2006["State"]=="UTTARAKHAND")]
df_st_UK_2006
Out[1148]:
In [1149]:
t_UK_2006 = df_st_UK_2006.loc[:,"Total"]
s6_52 = np.sum(t_UK_2006)
s6_52
Out[1149]:
In [1150]:
df_st_WB_2006 = df_2006.loc[(df_2006["State"]=="WEST BENGAL")]
df_st_WB_2006
Out[1150]:
In [1151]:
t_WB_2006 = df_st_WB_2006.loc[:,"Total"]
s6_53 = np.sum(t_WB_2006)
s6_53
Out[1151]:
In [1152]:
t_ANm_2006 = np.sum(df_st_AN_2006.where(df_st_AN_2006["Gender"]=="Male")["Total"])
t_ANm_2006
Out[1152]:
In [1153]:
t_ANfm_2006 = np.sum(df_st_AN_2006.where(df_st_AN_2006["Gender"]=="Female")["Total"])
t_ANfm_2006
Out[1153]:
In [1154]:
t_APm_2006 = np.sum(df_st_AP_2006.where(df_st_AP_2006["Gender"]=="Male")["Total"])
t_APm_2006
Out[1154]:
In [1155]:
t_APfm_2006 = np.sum(df_st_AP_2006.where(df_st_AP_2006["Gender"]=="Female")["Total"])
t_APfm_2006
Out[1155]:
In [1156]:
t_ARPm_2006 = np.sum(df_st_ARP_2006.where(df_st_ARP_2006["Gender"]=="Male")["Total"])
t_ARPm_2006
Out[1156]:
In [1157]:
t_ARPfm_2006 = np.sum(df_st_ARP_2006.where(df_st_ARP_2006["Gender"]=="Female")["Total"])
t_ARPfm_2006
Out[1157]:
In [1158]:
t_ASm_2006 = np.sum(df_st_AS_2006.where(df_st_AS_2006["Gender"]=="Male")["Total"])
t_ASm_2006
Out[1158]:
In [1159]:
t_ASfm_2006 = np.sum(df_st_AS_2006.where(df_st_AS_2006["Gender"]=="Female")["Total"])
t_ASfm_2006
Out[1159]:
In [1160]:
t_BHm_2006 = np.sum(df_st_BH_2006.where(df_st_BH_2006["Gender"]=="Male")["Total"])
t_BHm_2006
Out[1160]:
In [1161]:
t_BHfm_2006 = np.sum(df_st_BH_2006.where(df_st_BH_2006["Gender"]=="Female")["Total"])
t_BHfm_2006
Out[1161]:
In [1162]:
t_CHm_2006 = np.sum(df_st_CH_2006.where(df_st_CH_2006["Gender"]=="Male")["Total"])
t_CHm_2006
Out[1162]:
In [1163]:
t_CHfm_2006 = np.sum(df_st_CH_2006.where(df_st_CH_2006["Gender"]=="Female")["Total"])
t_CHfm_2006
Out[1163]:
In [1164]:
t_CTm_2006 = np.sum(df_st_CT_2006.where(df_st_CT_2006["Gender"]=="Male")["Total"])
t_CTm_2006
Out[1164]:
In [1165]:
t_CTfm_2006 = np.sum(df_st_CT_2006.where(df_st_CT_2006["Gender"]=="Female")["Total"])
t_CTfm_2006
Out[1165]:
In [1166]:
t_DNm_2006 = np.sum(df_st_DN_2006.where(df_st_DN_2006["Gender"]=="Male")["Total"])
t_DNm_2006
Out[1166]:
In [1167]:
t_DNfm_2006 = np.sum(df_st_DN_2006.where(df_st_DN_2006["Gender"]=="Female")["Total"])
t_DNfm_2006
Out[1167]:
In [1168]:
t_DDm_2006 = np.sum(df_st_DD_2006.where(df_st_DD_2006["Gender"]=="Male")["Total"])
t_DDm_2006
Out[1168]:
In [1169]:
t_DDfm_2006 = np.sum(df_st_DD_2006.where(df_st_DD_2006["Gender"]=="Female")["Total"])
t_DDfm_2006
Out[1169]:
In [1170]:
t_DLm_2006 = np.sum(df_st_DL_2006.where(df_st_DL_2006["Gender"]=="Male")["Total"])
t_DLm_2006
Out[1170]:
In [1171]:
t_DLfm_2006 = np.sum(df_st_DL_2006.where(df_st_DL_2006["Gender"]=="Female")["Total"])
t_DLfm_2006
Out[1171]:
In [1172]:
t_GOAm_2006 = np.sum(df_st_GOA_2006.where(df_st_GOA_2006["Gender"]=="Male")["Total"])
t_GOAm_2006
Out[1172]:
In [1173]:
t_GOAfm_2006 = np.sum(df_st_GOA_2006.where(df_st_GOA_2006["Gender"]=="Female")["Total"])
t_GOAfm_2006
Out[1173]:
In [1174]:
t_GJm_2006 = np.sum(df_st_GJ_2006.where(df_st_GJ_2006["Gender"]=="Male")["Total"])
t_GJm_2006
Out[1174]:
In [1175]:
t_GJfm_2006 = np.sum(df_st_GJ_2006.where(df_st_GJ_2006["Gender"]=="Female")["Total"])
t_GJfm_2006
Out[1175]:
In [1176]:
t_HRm_2006 = np.sum(df_st_HR_2006.where(df_st_HR_2006["Gender"]=="Male")["Total"])
t_HRm_2006
Out[1176]:
In [1177]:
t_HRfm_2006 = np.sum(df_st_HR_2006.where(df_st_HR_2006["Gender"]=="Female")["Total"])
t_HRfm_2006
Out[1177]:
In [1178]:
t_HPm_2006 = np.sum(df_st_HP_2006.where(df_st_HP_2006["Gender"]=="Male")["Total"])
t_HPm_2006
Out[1178]:
In [1179]:
t_HPfm_2006 = np.sum(df_st_HP_2006.where(df_st_HP_2006["Gender"]=="Female")["Total"])
t_HPfm_2006
Out[1179]:
In [1180]:
t_JKm_2006 = np.sum(df_st_JK_2006.where(df_st_JK_2006["Gender"]=="Male")["Total"])
t_JKm_2006
Out[1180]:
In [1181]:
t_JKfm_2006 = np.sum(df_st_JK_2006.where(df_st_JK_2006["Gender"]=="Female")["Total"])
t_JKfm_2006
Out[1181]:
In [1182]:
t_JHm_2006 = np.sum(df_st_JH_2006.where(df_st_JH_2006["Gender"]=="Male")["Total"])
t_JHm_2006
Out[1182]:
In [1183]:
t_JHfm_2006 = np.sum(df_st_JH_2006.where(df_st_JH_2006["Gender"]=="Female")["Total"])
t_JHfm_2006
Out[1183]:
In [1184]:
t_KNm_2006 = np.sum(df_st_KN_2006.where(df_st_KN_2006["Gender"]=="Male")["Total"])
t_KNm_2006
Out[1184]:
In [1185]:
t_KNfm_2006 = np.sum(df_st_KN_2006.where(df_st_KN_2006["Gender"]=="Female")["Total"])
t_KNfm_2006
Out[1185]:
In [1186]:
t_KERm_2006 = np.sum(df_st_KER_2006.where(df_st_KER_2006["Gender"]=="Male")["Total"])
t_KERm_2006
Out[1186]:
In [1187]:
t_KERfm_2006 = np.sum(df_st_KER_2006.where(df_st_KER_2006["Gender"]=="Female")["Total"])
t_KERfm_2006
Out[1187]:
In [1188]:
t_LDm_2006 = np.sum(df_st_LD_2006.where(df_st_LD_2006["Gender"]=="Male")["Total"])
t_LDm_2006
Out[1188]:
In [1189]:
t_LDfm_2006 = np.sum(df_st_LD_2006.where(df_st_LD_2006["Gender"]=="Female")["Total"])
t_LDfm_2006
Out[1189]:
In [1190]:
t_MPm_2006 = np.sum(df_st_MP_2006.where(df_st_MP_2006["Gender"]=="Male")["Total"])
t_MPm_2006
Out[1190]:
In [1191]:
t_MPfm_2006 = np.sum(df_st_MP_2006.where(df_st_MP_2006["Gender"]=="Female")["Total"])
t_MPfm_2006
Out[1191]:
In [1192]:
t_MHm_2006 = np.sum(df_st_MH_2006.where(df_st_MH_2006["Gender"]=="Male")["Total"])
t_MHm_2006
Out[1192]:
In [1193]:
t_MHfm_2006 = np.sum(df_st_MH_2006.where(df_st_MH_2006["Gender"]=="Female")["Total"])
t_MHfm_2006
Out[1193]:
In [1194]:
t_MNm_2006 = np.sum(df_st_MN_2006.where(df_st_MN_2006["Gender"]=="Male")["Total"])
t_MNm_2006
Out[1194]:
In [1195]:
t_MNfm_2006 = np.sum(df_st_MN_2006.where(df_st_MN_2006["Gender"]=="Female")["Total"])
t_MNfm_2006
Out[1195]:
In [1196]:
t_MGm_2006 = np.sum(df_st_MG_2006.where(df_st_MG_2006["Gender"]=="Male")["Total"])
t_MGm_2006
Out[1196]:
In [1197]:
t_MGfm_2006 = np.sum(df_st_MG_2006.where(df_st_MG_2006["Gender"]=="Female")["Total"])
t_MGfm_2006
Out[1197]:
In [1198]:
t_MZm_2006 = np.sum(df_st_MZ_2006.where(df_st_MZ_2006["Gender"]=="Male")["Total"])
t_MZm_2006
Out[1198]:
In [1199]:
t_MZfm_2006 = np.sum(df_st_MZ_2006.where(df_st_MZ_2006["Gender"]=="Female")["Total"])
t_MZfm_2006
Out[1199]:
In [1200]:
t_NGm_2006 = np.sum(df_st_NG_2006.where(df_st_NG_2006["Gender"]=="Male")["Total"])
t_NGm_2006
Out[1200]:
In [1201]:
t_NGfm_2006 = np.sum(df_st_NG_2006.where(df_st_NG_2006["Gender"]=="Female")["Total"])
t_MGfm_2006
Out[1201]:
In [1202]:
t_ODm_2006 = np.sum(df_st_OD_2006.where(df_st_OD_2006["Gender"]=="Male")["Total"])
t_ODm_2006
Out[1202]:
In [1203]:
t_ODfm_2006 = np.sum(df_st_OD_2006.where(df_st_OD_2006["Gender"]=="Female")["Total"])
t_ODfm_2006
Out[1203]:
In [1204]:
t_PDm_2006 = np.sum(df_st_PD_2006.where(df_st_PD_2006["Gender"]=="Male")["Total"])
t_PDm_2006
Out[1204]:
In [1205]:
t_PDfm_2006 = np.sum(df_st_PD_2006.where(df_st_PD_2006["Gender"]=="Female")["Total"])
t_PDfm_2006
Out[1205]:
In [1206]:
t_PBm_2006 = np.sum(df_st_PB_2006.where(df_st_PB_2006["Gender"]=="Male")["Total"])
t_PBm_2006
Out[1206]:
In [1207]:
t_PBfm_2006 = np.sum(df_st_PB_2006.where(df_st_PB_2006["Gender"]=="Female")["Total"])
t_PBfm_2006
Out[1207]:
In [1208]:
t_RJm_2006 = np.sum(df_st_RJ_2006.where(df_st_RJ_2006["Gender"]=="Male")["Total"])
t_RJm_2006
Out[1208]:
In [1209]:
t_RJfm_2006 = np.sum(df_st_RJ_2006.where(df_st_RJ_2006["Gender"]=="Female")["Total"])
t_RJfm_2006
Out[1209]:
In [1210]:
t_SMm_2006 = np.sum(df_st_SM_2006.where(df_st_SM_2006["Gender"]=="Male")["Total"])
t_SMm_2006
Out[1210]:
In [1211]:
t_SMfm_2006 = np.sum(df_st_SM_2006.where(df_st_SM_2006["Gender"]=="Female")["Total"])
t_SMfm_2006
Out[1211]:
In [1212]:
t_TNm_2006 = np.sum(df_st_TN_2006.where(df_st_TN_2006["Gender"]=="Male")["Total"])
t_TNm_2006
Out[1212]:
In [1213]:
t_TNfm_2006 = np.sum(df_st_TN_2006.where(df_st_TN_2006["Gender"]=="Female")["Total"])
t_TNfm_2006
Out[1213]:
In [1214]:
t_TPm_2006 = np.sum(df_st_TP_2006.where(df_st_TP_2006["Gender"]=="Male")["Total"])
t_TPm_2006
Out[1214]:
In [1215]:
t_TPfm_2006 = np.sum(df_st_TP_2006.where(df_st_TP_2006["Gender"]=="Female")["Total"])
t_TPfm_2006
Out[1215]:
In [1216]:
t_UPm_2006 = np.sum(df_st_UP_2006.where(df_st_UP_2006["Gender"]=="Male")["Total"])
t_UPm_2006
Out[1216]:
In [1217]:
t_UPfm_2006 = np.sum(df_st_UP_2006.where(df_st_UP_2006["Gender"]=="Female")["Total"])
t_UPfm_2006
Out[1217]:
In [1218]:
t_UKm_2006 = np.sum(df_st_UK_2006.where(df_st_UK_2006["Gender"]=="Male")["Total"])
t_UKm_2006
Out[1218]:
In [1219]:
t_UKfm_2006 = np.sum(df_st_UK_2006.where(df_st_UK_2006["Gender"]=="Female")["Total"])
t_UKfm_2006
Out[1219]:
In [1220]:
t_WBm_2006 = np.sum(df_st_WB_2006.where(df_st_WB_2006["Gender"]=="Male")["Total"])
t_WBm_2006
Out[1220]:
In [1221]:
t_WBfm_2006 = np.sum(df_st_WB_2006.where(df_st_WB_2006["Gender"]=="Female")["Total"])
t_WBfm_2006
Out[1221]:
In [1222]:
l_2006=list(df_2006.loc[:,"Type"].unique())
l_2006.sort()
print(l_2006,end=" ")
In [1223]:
cause_l_2006=[]
for i in l_2006:
rough= np.sum(df_2006.where(df_2006["Type"]==i)["Total"])
cause_l_2006.append(rough)
print(cause_l_2006,end=" ")
In [1224]:
x1_2006 = set(df_2006.loc[:,"State"])
x1_2006
len(x1_2006)
Out[1224]:
In [1225]:
cause_df_2006 = pd.DataFrame(cause_l_2006,l_2006)
cause_df_2006.reset_index()
Out[1225]:
In [1226]:
cause_df_2006.plot.bar(stacked=True,figsize=(50,20))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("Causes",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths due to Different Causes in 2006",fontsize=55)
plt.legend(["Death Count"], prop={"size":30})
Out[1226]:
In [1227]:
s_2006=list(df_2006.loc[:,"State"].unique())
s_2006.sort()
print(s_2006,end=" ")
In [1228]:
state_s_2006 = [s6_19,s6_20,s6_21,s6_22,s6_23,s6_24,s6_25,s6_26,s6_27,s6_28,s6_29,s6_30,s6_31,s6_32,s6_33,s6_34,
s6_35,s6_36,s6_37,s6_38,s6_39,s6_40,s6_41,s6_42,s6_43,s6_44,s6_45,s6_46,s6_47,s6_48,s6_49,s6_50,
s6_51,s6_52,s6_53]
print(state_s_2006,end=" ")
In [1229]:
state_m_2006=[t_ANm_2006, t_APm_2006, t_ARPm_2006, t_ASm_2006, t_BHm_2006, t_CHm_2006, t_CTm_2006, t_DNm_2006,
t_DDm_2006, t_DLm_2006, t_GOAm_2006, t_GJm_2006, t_HRm_2006, t_HPm_2006,t_JKm_2006, t_JHm_2006,
t_KNm_2006, t_KERm_2006, t_LDm_2006,t_MPm_2006,t_MHm_2006,t_MNm_2006,t_MGm_2006,t_MZm_2006,
t_NGm_2006, t_ODm_2006, t_PDm_2006,t_PBm_2006,t_RJm_2006,t_SMm_2006,t_TNm_2006,t_TPm_2006,
t_UPm_2006, t_UKm_2006, t_WBm_2006]
print(state_m_2006,end=" ")
In [1230]:
state_fm_2006=[t_ANfm_2006, t_APfm_2006, t_ARPfm_2006, t_ASfm_2006, t_BHfm_2006, t_CHfm_2006, t_CTfm_2006, t_DNfm_2006,
t_DDfm_2006, t_DLfm_2006, t_GOAfm_2006, t_GJfm_2006, t_HRfm_2006, t_HPfm_2006,t_JKfm_2006, t_JHfm_2006,
t_KNfm_2006, t_KERfm_2006, t_LDfm_2006,t_MPfm_2006,t_MHfm_2006,t_MNfm_2006,t_MGfm_2006,t_MZfm_2006,
t_NGfm_2006, t_ODfm_2006, t_PDfm_2006,t_PBfm_2006,t_RJfm_2006,t_SMfm_2006,t_TNfm_2006,t_TPfm_2006,
t_UPfm_2006, t_UKfm_2006, t_WBfm_2006]
print(state_fm_2006,end=" ")
In [1231]:
df_state_2006 = {
"State":['A & N ISLANDS', 'ANDHRA PRADESH', 'ARUNACHAL PRADESH', 'ASSAM', 'BIHAR', 'CHANDIGARH', 'CHHATTISGARH',
'D & N HAVELI', 'DAMAN & DIU', 'DELHI (UT)', 'GOA', 'GUJARAT', 'HARYANA', 'HIMACHAL PRADESH', 'JAMMU & KASHMIR',
'JHARKHAND', 'KARNATAKA', 'KERALA', 'LAKSHADWEEP', 'MADHYA PRADESH', 'MAHARASHTRA', 'MANIPUR', 'MEGHALAYA',
'MIZORAM', 'NAGALAND', 'ODISHA', 'PUDUCHERRY', 'PUNJAB', 'RAJASTHAN', 'SIKKIM', 'TAMIL NADU', 'TRIPURA',
'UTTAR PRADESH', 'UTTARAKHAND', 'WEST BENGAL'],
"Total":[s6_19,s6_20,s6_21,s6_22,s6_23,s6_24,s6_25,s6_26,s6_27,s6_28,s6_29,s6_30,s6_31,s6_32,s6_33,s6_34,
s6_35,s6_36,s6_37,s6_38,s6_39,s6_40,s6_41,s6_42,s6_43,s6_44,s6_45,s6_46,s6_47,s6_48,s6_49,s6_50,
s6_51,s6_52,s6_53],
"Male":[t_ANm_2006, t_APm_2006, t_ARPm_2006, t_ASm_2006, t_BHm_2006, t_CHm_2006, t_CTm_2006, t_DNm_2006,
t_DDm_2006, t_DLm_2006, t_GOAm_2006, t_GJm_2006, t_HRm_2006, t_HPm_2006,t_JKm_2006, t_JHm_2006,
t_KNm_2006, t_KERm_2006, t_LDm_2006,t_MPm_2006,t_MHm_2006,t_MNm_2006,t_MGm_2006,t_MZm_2006,
t_NGm_2006, t_ODm_2006, t_PDm_2006,t_PBm_2006,t_RJm_2006,t_SMm_2006,t_TNm_2006,t_TPm_2006,
t_UPm_2006, t_UKm_2006, t_WBm_2006],
"Female":[t_ANfm_2006, t_APfm_2006, t_ARPfm_2006, t_ASfm_2006, t_BHfm_2006, t_CHfm_2006, t_CTfm_2006, t_DNfm_2006,
t_DDfm_2006, t_DLfm_2006, t_GOAfm_2006, t_GJfm_2006, t_HRfm_2006, t_HPfm_2006,t_JKfm_2006, t_JHfm_2006,
t_KNfm_2006, t_KERfm_2006, t_LDfm_2006,t_MPfm_2006,t_MHfm_2006,t_MNfm_2006,t_MGfm_2006,t_MZfm_2006,
t_NGfm_2006, t_ODfm_2006, t_PDfm_2006,t_PBfm_2006,t_RJfm_2006,t_SMfm_2006,t_TNfm_2006,t_TPfm_2006,
t_UPfm_2006, t_UKfm_2006, t_WBfm_2006]
}
df_All_st_2006 = pd.DataFrame(df_state_2006)
df_All_st_2006
Out[1231]:
In [1232]:
pivot_2006 = pd.pivot_table(df_All_st_2006, values=None, index='State', columns=None,
fill_value=None, margins=False, dropna=True, margins_name='All', observed=False)
pivot_2006
Out[1232]:
In [1233]:
pivot_2006.plot(kind='bar',figsize=(60,30))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("States",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths of Males And Females In Different State",fontsize=55)
plt.legend(["Female","Male","Total"], prop={"size":30})
plt.show
Out[1233]:
In [1234]:
cause_l_percent_2006=[]
for i in cause_l_2006:
p=i/np.sum(cause_l_2006)
avg_2006_cause=p*100
cause_l_percent_2006.append(avg_2006_cause)
print(cause_l_percent_2006,end=" ")
In [1235]:
np.max(cause_l_percent_2006) #Married
Out[1235]:
In [1236]:
np.min(cause_l_percent_2006) #Ideological Causes/Hero Worshipping
Out[1236]:
In [1237]:
state_s_2006_percent=[]
tot=np.sum(state_s_2006)
for i in state_s_2006:
xy=i/tot
avg_state_2006=xy*100
state_s_2006_percent.append(avg_state_2006)
print(state_s_2006_percent,end=" ")
In [1238]:
np.max(state_s_2006_percent) #Maharashtra
Out[1238]:
In [1239]:
np.min(state_s_2006_percent) #Lakshdweep
Out[1239]:
In [1240]:
l_age_grp_2006=[s6_4,s6_5,s6_6,s6_7,s6_8,s6_54]
l_age_grp_2006
Out[1240]:
In [1241]:
l_age_grp_2006_percent=[]
for i in l_age_grp_2006:
yz=i/s6_1
avg_2006_age_grp=yz*100
l_age_grp_2006_percent.append(avg_2006_age_grp)
l_age_grp_2006_percent
Out[1241]:
In [1242]:
np.max(l_age_grp_2006_percent) #0-100+
Out[1242]:
In [1243]:
np.min(l_age_grp_2006_percent) #0-14
Out[1243]:
In [1244]:
total_male_percent_2006= (s6_2/s6_1)*100
total_male_percent_2006
Out[1244]:
In [1245]:
total_female_percent_2006= (s6_3/s6_1)*100
total_female_percent_2006
Out[1245]:
In [ ]:
2007¶
In [1246]:
df_2007 = suicide_df.loc[(suicide_df["Year"]==2007)]
df_2007
Out[1246]:
In [1247]:
t2007_dcount = df_2007.loc[:,"Total"]
s7_1 = np.sum(t2007_dcount)
s7_1
Out[1247]:
In [1248]:
male_2007 = df_2007.loc[(df_2007["Gender"]=="Male")]
male_2007
Out[1248]:
In [1249]:
m2007_count = male_2007.loc[:,"Total"]
s7_2 = np.sum(m2007_count)
s7_2
Out[1249]:
In [1250]:
female_2007 = df_2007.loc[(df_2007["Gender"]=="Female")]
female_2007
Out[1250]:
In [1251]:
fm2007_count = female_2007.loc[:,"Total"]
s7_3 = np.sum(fm2007_count)
s7_3
Out[1251]:
In [1252]:
df_age_d014_2007 = df_2007.loc[(df_2007["Age_group"]=="0-14")]
df_age_d014_2007
Out[1252]:
In [1253]:
t_014_2007 = df_age_d014_2007.loc[:,"Total"]
s7_4 = np.sum(t_014_2007)
s7_4
Out[1253]:
In [1254]:
df_age_d1529_2007 = df_2007.loc[(df_2007["Age_group"]=="15-29")]
df_age_d1529_2007
Out[1254]:
In [1255]:
t_1529_2007 = df_age_d1529_2007.loc[:,"Total"]
s7_5 = np.sum(t_1529_2007)
s7_5
Out[1255]:
In [1256]:
df_age_d3044_2007 = df_2007.loc[(df_2007["Age_group"]=="30-44")]
df_age_d3044_2007
Out[1256]:
In [1257]:
t_3044_2007 = df_age_d3044_2007.loc[:,"Total"]
s7_6 = np.sum(t_3044_2007)
s7_6
Out[1257]:
In [1258]:
df_age_d4559_2007 = df_2007.loc[(df_2007["Age_group"]=="45-59")]
df_age_d4559_2007
Out[1258]:
In [1259]:
t_4559_2007 = df_age_d4559_2007.loc[:,"Total"]
s7_7 = np.sum(t_4559_2007)
s7_7
Out[1259]:
In [1260]:
df_age_d60_2007 = df_2007.loc[(df_2007["Age_group"]=="60+")]
df_age_d60_2007
Out[1260]:
In [1261]:
t_60_2007 = df_age_d60_2007.loc[:,"Total"]
s7_8 = np.sum(t_60_2007)
s7_8
Out[1261]:
In [1262]:
df_age_d0100_2007 = df_2007.loc[(df_2007["Age_group"]=="0-100+")]
df_age_d0100_2007
Out[1262]:
In [1263]:
t_0100_2007 = df_age_d0100_2007.loc[:,"Total"]
s7_54 = np.sum(t_0100_2007)
s7_54
Out[1263]:
In [1264]:
df_014m_2007 = df_age_d014_2007.loc[(df_age_d014_2007["Gender"]=="Male")]
df_014m_2007
Out[1264]:
In [1265]:
t_014m_2007 = df_014m_2007.loc[:,"Total"]
s7_9 = np.sum(t_014m_2007)
s7_9
Out[1265]:
In [1266]:
df_014fm_2007 = df_age_d014_2007.loc[(df_age_d014_2007["Gender"]=="Female")]
df_014fm_2007
Out[1266]:
In [1267]:
t_014fm_2007 = df_014fm_2007.loc[:,"Total"]
s7_10 = np.sum(t_014fm_2007)
s7_10
Out[1267]:
In [1268]:
df_1529m_2007 = df_age_d1529_2007.loc[(df_age_d1529_2007["Gender"]=="Male")]
df_1529m_2007
Out[1268]:
In [1269]:
t_1529m_2007 = df_1529m_2007.loc[:,"Total"]
s7_11 = np.sum(t_1529m_2007)
s7_11
Out[1269]:
In [1270]:
df_1529fm_2007 = df_age_d1529_2007.loc[(df_age_d1529_2007["Gender"]=="Female")]
df_1529fm_2007
Out[1270]:
In [1271]:
t_1529fm_2007 = df_1529fm_2007.loc[:,"Total"]
s7_12 = np.sum(t_1529fm_2007)
s7_12
Out[1271]:
In [1272]:
df_3044m_2007 = df_age_d3044_2007.loc[(df_age_d3044_2007["Gender"]=="Male")]
df_3044m_2007
Out[1272]:
In [1273]:
t_3044m_2007 = df_3044m_2007.loc[:,"Total"]
s7_13 = np.sum(t_3044m_2007)
s7_13
Out[1273]:
In [1274]:
df_3044fm_2007 = df_age_d3044_2007.loc[(df_age_d3044_2007["Gender"]=="Female")]
df_3044fm_2007
Out[1274]:
In [1275]:
t_3044fm_2007 = df_3044fm_2007.loc[:,"Total"]
s7_14 = np.sum(t_3044fm_2007)
s7_14
Out[1275]:
In [1276]:
df_4559m_2007 = df_age_d4559_2007.loc[(df_age_d4559_2007["Gender"]=="Male")]
df_4559m_2007
Out[1276]:
In [1277]:
t_4559m_2007 = df_4559m_2007.loc[:,"Total"]
s7_15 = np.sum(t_4559m_2007)
s7_15
Out[1277]:
In [1278]:
df_4559fm_2007 = df_age_d4559_2007.loc[(df_age_d4559_2007["Gender"]=="Female")]
df_4559fm_2007
Out[1278]:
In [1279]:
t_4559fm_2007 = df_4559fm_2007.loc[:,"Total"]
s7_16 = np.sum(t_4559fm_2007)
s7_16
Out[1279]:
In [1280]:
df_60m_2007 = df_age_d60_2007.loc[(df_age_d60_2007["Gender"]=="Male")]
df_60m_2007
Out[1280]:
In [1281]:
t_60m_2007 = df_60m_2007.loc[:,"Total"]
s7_17 = np.sum(t_60m_2007)
s7_17
Out[1281]:
In [1282]:
df_60fm_2007 = df_age_d60_2007.loc[(df_age_d60_2007["Gender"]=="Female")]
df_60fm_2007
Out[1282]:
In [1283]:
t_60fm_2007 = df_60fm_2007.loc[:,"Total"]
s7_18 = np.sum(t_60fm_2007)
s7_18
Out[1283]:
In [1284]:
df_0100fm_2007 = df_age_d0100_2007.loc[(df_age_d0100_2007["Gender"]=="Female")]
df_0100fm_2007
Out[1284]:
In [1285]:
t_0100fm_2007 = df_0100fm_2007.loc[:,"Total"]
s7_55 = np.sum(t_0100fm_2007)
s7_55
Out[1285]:
In [1286]:
df_0100m_2007 = df_age_d0100_2007.loc[(df_age_d0100_2007["Gender"]=="Male")]
df_0100m_2007
Out[1286]:
In [1287]:
t_0100m_2007 = df_0100m_2007.loc[:,"Total"]
s7_56 = np.sum(t_0100m_2007)
s7_56
Out[1287]:
In [1288]:
df_st_AN_2007 = df_2007.loc[(df_2007["State"]=="A & N ISLANDS")]
df_st_AN_2007
Out[1288]:
In [1289]:
t_AN_2007 = df_st_AN_2007.loc[:,"Total"]
s7_19 = np.sum(t_AN_2007)
s7_19
Out[1289]:
In [1290]:
df_st_AP_2007 = df_2007.loc[(df_2007["State"]=="ANDHRA PRADESH")]
df_st_AP_2007
Out[1290]:
In [1291]:
t_AP_2007 = df_st_AP_2007.loc[:,"Total"]
s7_20 = np.sum(t_AP_2007)
s7_20
Out[1291]:
In [1292]:
df_st_ARP_2007 = df_2007.loc[(df_2007["State"]=="ARUNACHAL PRADESH")]
df_st_ARP_2007
Out[1292]:
In [1293]:
t_ARP_2007 = df_st_ARP_2007.loc[:,"Total"]
s7_21 = np.sum(t_ARP_2007)
s7_21
Out[1293]:
In [1294]:
df_st_AS_2007 = df_2007.loc[(df_2007["State"]=="ASSAM")]
df_st_AS_2007
Out[1294]:
In [1295]:
t_AS_2007 = df_st_AS_2007.loc[:,"Total"]
s7_22 = np.sum(t_AS_2007)
s7_22
Out[1295]:
In [1296]:
df_st_BH_2007 = df_2007.loc[(df_2007["State"]=="BIHAR")]
df_st_BH_2007
Out[1296]:
In [1297]:
t_BH_2007 = df_st_BH_2007.loc[:,"Total"]
s7_23 = np.sum(t_BH_2007)
s7_23
Out[1297]:
In [1298]:
df_st_CH_2007 = df_2007.loc[(df_2007["State"]=="CHANDIGARH")]
df_st_CH_2007
Out[1298]:
In [1299]:
t_CH_2007 = df_st_CH_2007.loc[:,"Total"]
s7_24 = np.sum(t_CH_2007)
s7_24
Out[1299]:
In [1300]:
df_st_CT_2007 = df_2007.loc[(df_2007["State"]=="CHHATTISGARH")]
df_st_CT_2007
Out[1300]:
In [1301]:
t_CT_2007 = df_st_CT_2007.loc[:,"Total"]
s7_25 = np.sum(t_CT_2007)
s7_25
Out[1301]:
In [1302]:
df_st_DN_2007 = df_2007.loc[(df_2007["State"]=="D & N HAVELI")]
df_st_DN_2007
Out[1302]:
In [1303]:
t_DN_2007 = df_st_DN_2007.loc[:,"Total"]
s7_26 = np.sum(t_DN_2007)
s7_26
Out[1303]:
In [1304]:
df_st_DD_2007 = df_2007.loc[(df_2007["State"]=="DAMAN & DIU")]
df_st_DD_2007
Out[1304]:
In [1305]:
t_DD_2007 = df_st_DD_2007.loc[:,"Total"]
s7_27 = np.sum(t_DD_2007)
s7_27
Out[1305]:
In [1306]:
df_st_DL_2007 = df_2007.loc[(df_2007["State"]=="DELHI (UT)")]
df_st_DL_2007
Out[1306]:
In [1307]:
t_DL_2007 = df_st_DL_2007.loc[:,"Total"]
s7_28 = np.sum(t_DL_2007)
s7_28
Out[1307]:
In [1308]:
df_st_GOA_2007 = df_2007.loc[(df_2007["State"]=="GOA")]
df_st_GOA_2007
Out[1308]:
In [1309]:
t_GOA_2007 = df_st_GOA_2007.loc[:,"Total"]
s7_29 = np.sum(t_GOA_2007)
s7_29
Out[1309]:
In [1310]:
df_st_GJ_2007 = df_2007.loc[(df_2007["State"]=="GUJARAT")]
df_st_GJ_2007
Out[1310]:
In [1311]:
t_GJ_2007 = df_st_GJ_2007.loc[:,"Total"]
s7_30 = np.sum(t_GJ_2007)
s7_30
Out[1311]:
In [1312]:
df_st_HR_2007 = df_2007.loc[(df_2007["State"]=="HARYANA")]
df_st_HR_2007
Out[1312]:
In [1313]:
t_HR_2007 = df_st_HR_2007.loc[:,"Total"]
s7_31 = np.sum(t_HR_2007)
s7_31
Out[1313]:
In [1314]:
df_st_HP_2007 = df_2007.loc[(df_2007["State"]=="HIMACHAL PRADESH")]
df_st_HP_2007
Out[1314]:
In [1315]:
t_HP_2007 = df_st_HP_2007.loc[:,"Total"]
s7_32 = np.sum(t_HP_2007)
s7_32
Out[1315]:
In [1316]:
df_st_JK_2007 = df_2007.loc[(df_2007["State"]=="JAMMU & KASHMIR")]
df_st_JK_2007
Out[1316]:
In [1317]:
t_JK_2007 = df_st_JK_2007.loc[:,"Total"]
s7_33 = np.sum(t_JK_2007)
s7_33
Out[1317]:
In [1318]:
df_st_JH_2007 = df_2007.loc[(df_2007["State"]=="JHARKHAND")]
df_st_JH_2007
Out[1318]:
In [1319]:
t_JH_2007 = df_st_JH_2007.loc[:,"Total"]
s7_34 = np.sum(t_JH_2007)
s7_34
Out[1319]:
In [1320]:
df_st_KN_2007 = df_2007.loc[(df_2007["State"]=="KARNATAKA")]
df_st_KN_2007
Out[1320]:
In [1321]:
t_KN_2007 = df_st_KN_2007.loc[:,"Total"]
s7_35 = np.sum(t_KN_2007)
s7_35
Out[1321]:
In [1322]:
df_st_KER_2007 = df_2007.loc[(df_2007["State"]=="KERALA")]
df_st_KER_2007
Out[1322]:
In [1323]:
t_KER_2007 = df_st_KER_2007.loc[:,"Total"]
s7_36 = np.sum(t_KER_2007)
s7_36
Out[1323]:
In [1324]:
df_st_LD_2007 = df_2007.loc[(df_2007["State"]=="LAKSHADWEEP")]
df_st_LD_2007
Out[1324]:
In [1325]:
t_LD_2007 = df_st_LD_2007.loc[:,"Total"]
s7_37 = np.sum(t_LD_2007)
s7_37
Out[1325]:
In [1326]:
df_st_MP_2007 = df_2007.loc[(df_2007["State"]=="MADHYA PRADESH")]
df_st_MP_2007
Out[1326]:
In [1327]:
t_MP_2007 = df_st_MP_2007.loc[:,"Total"]
s7_38 = np.sum(t_MP_2007)
s7_38
Out[1327]:
In [1328]:
df_st_MH_2007 = df_2007.loc[(df_2007["State"]=="MAHARASHTRA")]
df_st_MH_2007
Out[1328]:
In [1329]:
t_MH_2007 = df_st_MH_2007.loc[:,"Total"]
s7_39 = np.sum(t_MH_2007)
s7_39
Out[1329]:
In [1330]:
df_st_MN_2007 = df_2007.loc[(df_2007["State"]=="MANIPUR")]
df_st_MN_2007
Out[1330]:
In [1331]:
t_MN_2007 = df_st_MN_2007.loc[:,"Total"]
s7_40 = np.sum(t_MN_2007)
s7_40
Out[1331]:
In [1332]:
df_st_MG_2007 = df_2007.loc[(df_2007["State"]=="MEGHALAYA")]
df_st_MG_2007
Out[1332]:
In [1333]:
t_MG_2007 = df_st_MG_2007.loc[:,"Total"]
s7_41 = np.sum(t_MG_2007)
s7_41
Out[1333]:
In [1334]:
df_st_MZ_2007 = df_2007.loc[(df_2007["State"]=="MIZORAM")]
df_st_MZ_2007
Out[1334]:
In [1335]:
t_MN_2007 = df_st_MN_2007.loc[:,"Total"]
s7_42 = np.sum(t_MN_2007)
s7_42
Out[1335]:
In [1336]:
df_st_NG_2007 = df_2007.loc[(df_2007["State"]=="NAGALAND")]
df_st_NG_2007
Out[1336]:
In [1337]:
t_NG_2007 = df_st_NG_2007.loc[:,"Total"]
s7_43 = np.sum(t_NG_2007)
s7_43
Out[1337]:
In [1338]:
df_st_OD_2007 = df_2007.loc[(df_2007["State"]=="ODISHA")]
df_st_OD_2007
Out[1338]:
In [1339]:
t_OD_2007 = df_st_OD_2007.loc[:,"Total"]
s7_44 = np.sum(t_OD_2007)
s7_44
Out[1339]:
In [1340]:
df_st_PD_2007 = df_2007.loc[(df_2007["State"]=="PUDUCHERRY")]
df_st_PD_2007
Out[1340]:
In [1341]:
t_PD_2007 = df_st_PD_2007.loc[:,"Total"]
s7_45 = np.sum(t_PD_2007)
s7_45
Out[1341]:
In [1342]:
df_st_PB_2007 = df_2007.loc[(df_2007["State"]=="PUNJAB")]
df_st_PB_2007
Out[1342]:
In [1343]:
t_PB_2007 = df_st_PB_2007.loc[:,"Total"]
s7_46 = np.sum(t_PB_2007)
s7_46
Out[1343]:
In [1344]:
df_st_RJ_2007 = df_2007.loc[(df_2007["State"]=="RAJASTHAN")]
df_st_RJ_2007
Out[1344]:
In [1345]:
t_RJ_2007 = df_st_RJ_2007.loc[:,"Total"]
s7_47 = np.sum(t_RJ_2007)
s7_47
Out[1345]:
In [1346]:
df_st_SM_2007 = df_2007.loc[(df_2007["State"]=="SIKKIM")]
df_st_SM_2007
Out[1346]:
In [1347]:
t_SM_2007 = df_st_SM_2007.loc[:,"Total"]
s7_48 = np.sum(t_SM_2007)
s7_48
Out[1347]:
In [1348]:
df_st_TN_2007 = df_2007.loc[(df_2007["State"]=="TAMIL NADU")]
df_st_TN_2007
Out[1348]:
In [1349]:
t_TN_2007 = df_st_TN_2007.loc[:,"Total"]
s7_49 = np.sum(t_TN_2007)
s7_49
Out[1349]:
In [1350]:
df_st_TP_2007 = df_2007.loc[(df_2007["State"]=="TRIPURA")]
df_st_TP_2007
Out[1350]:
In [1351]:
t_TP_2007 = df_st_TP_2007.loc[:,"Total"]
s7_50 = np.sum(t_TP_2007)
s7_50
Out[1351]:
In [1352]:
df_st_UP_2007 = df_2007.loc[(df_2007["State"]=="UTTAR PRADESH")]
df_st_UP_2007
Out[1352]:
In [1353]:
t_UP_2007 = df_st_UP_2007.loc[:,"Total"]
s7_51 = np.sum(t_UP_2007)
s7_51
Out[1353]:
In [1354]:
df_st_UK_2007 = df_2007.loc[(df_2007["State"]=="UTTARAKHAND")]
df_st_UK_2007
Out[1354]:
In [1355]:
t_UK_2007 = df_st_UK_2007.loc[:,"Total"]
s7_52 = np.sum(t_UK_2007)
s7_52
Out[1355]:
In [1356]:
df_st_WB_2007 = df_2007.loc[(df_2007["State"]=="WEST BENGAL")]
df_st_WB_2007
Out[1356]:
In [1357]:
t_WB_2007 = df_st_WB_2007.loc[:,"Total"]
s7_53 = np.sum(t_WB_2007)
s7_53
Out[1357]:
In [1358]:
t_ANm_2007 = np.sum(df_st_AN_2007.where(df_st_AN_2007["Gender"]=="Male")["Total"])
t_ANm_2007
Out[1358]:
In [1359]:
t_ANfm_2007 = np.sum(df_st_AN_2007.where(df_st_AN_2007["Gender"]=="Female")["Total"])
t_ANfm_2007
Out[1359]:
In [1360]:
t_APm_2007 = np.sum(df_st_AP_2007.where(df_st_AP_2007["Gender"]=="Male")["Total"])
t_APm_2007
Out[1360]:
In [1361]:
t_APfm_2007 = np.sum(df_st_AP_2007.where(df_st_AP_2007["Gender"]=="Female")["Total"])
t_APfm_2007
Out[1361]:
In [1362]:
t_ARPm_2007 = np.sum(df_st_ARP_2007.where(df_st_ARP_2007["Gender"]=="Male")["Total"])
t_ARPm_2007
Out[1362]:
In [1363]:
t_ARPfm_2007 = np.sum(df_st_ARP_2007.where(df_st_ARP_2007["Gender"]=="Female")["Total"])
t_ARPfm_2007
Out[1363]:
In [1364]:
t_ASm_2007 = np.sum(df_st_AS_2007.where(df_st_AS_2007["Gender"]=="Male")["Total"])
t_ASm_2007
Out[1364]:
In [1365]:
t_ASfm_2007 = np.sum(df_st_AS_2007.where(df_st_AS_2007["Gender"]=="Female")["Total"])
t_ASfm_2007
Out[1365]:
In [1366]:
t_BHm_2007 = np.sum(df_st_BH_2007.where(df_st_BH_2007["Gender"]=="Male")["Total"])
t_BHm_2007
Out[1366]:
In [1367]:
t_BHfm_2007 = np.sum(df_st_BH_2007.where(df_st_BH_2007["Gender"]=="Female")["Total"])
t_BHfm_2007
Out[1367]:
In [1368]:
t_CHm_2007 = np.sum(df_st_CH_2007.where(df_st_CH_2007["Gender"]=="Male")["Total"])
t_CHm_2007
Out[1368]:
In [1369]:
t_CHfm_2007 = np.sum(df_st_CH_2007.where(df_st_CH_2007["Gender"]=="Female")["Total"])
t_CHfm_2007
Out[1369]:
In [1370]:
t_CTm_2007 = np.sum(df_st_CT_2007.where(df_st_CT_2007["Gender"]=="Male")["Total"])
t_CTm_2007
Out[1370]:
In [1371]:
t_CTfm_2007 = np.sum(df_st_CT_2007.where(df_st_CT_2007["Gender"]=="Female")["Total"])
t_CTfm_2007
Out[1371]:
In [1372]:
t_DNm_2007 = np.sum(df_st_DN_2007.where(df_st_DN_2007["Gender"]=="Male")["Total"])
t_DNm_2007
Out[1372]:
In [1373]:
t_DNfm_2007 = np.sum(df_st_DN_2007.where(df_st_DN_2007["Gender"]=="Female")["Total"])
t_DNfm_2007
Out[1373]:
In [1374]:
t_DDm_2007 = np.sum(df_st_DD_2007.where(df_st_DD_2007["Gender"]=="Male")["Total"])
t_DDm_2007
Out[1374]:
In [1375]:
t_DDfm_2007 = np.sum(df_st_DD_2007.where(df_st_DD_2007["Gender"]=="Female")["Total"])
t_DDfm_2007
Out[1375]:
In [1376]:
t_DLm_2007 = np.sum(df_st_DL_2007.where(df_st_DL_2007["Gender"]=="Male")["Total"])
t_DLm_2007
Out[1376]:
In [1377]:
t_DLfm_2007 = np.sum(df_st_DL_2007.where(df_st_DL_2007["Gender"]=="Female")["Total"])
t_DLfm_2007
Out[1377]:
In [1378]:
t_GOAm_2007 = np.sum(df_st_GOA_2007.where(df_st_GOA_2007["Gender"]=="Male")["Total"])
t_GOAm_2007
Out[1378]:
In [1379]:
t_GOAfm_2007 = np.sum(df_st_GOA_2007.where(df_st_GOA_2007["Gender"]=="Female")["Total"])
t_GOAfm_2007
Out[1379]:
In [1380]:
t_GJm_2007 = np.sum(df_st_GJ_2007.where(df_st_GJ_2007["Gender"]=="Male")["Total"])
t_GJm_2007
Out[1380]:
In [1381]:
t_GJfm_2007 = np.sum(df_st_GJ_2007.where(df_st_GJ_2007["Gender"]=="Female")["Total"])
t_GJfm_2007
Out[1381]:
In [1382]:
t_HRm_2007 = np.sum(df_st_HR_2007.where(df_st_HR_2007["Gender"]=="Male")["Total"])
t_HRm_2007
Out[1382]:
In [1383]:
t_HRfm_2007 = np.sum(df_st_HR_2007.where(df_st_HR_2007["Gender"]=="Female")["Total"])
t_HRfm_2007
Out[1383]:
In [1384]:
t_HPm_2007 = np.sum(df_st_HP_2007.where(df_st_HP_2007["Gender"]=="Male")["Total"])
t_HPm_2007
Out[1384]:
In [1385]:
t_HPfm_2007 = np.sum(df_st_HP_2007.where(df_st_HP_2007["Gender"]=="Female")["Total"])
t_HPfm_2007
Out[1385]:
In [1386]:
t_JKm_2007 = np.sum(df_st_JK_2007.where(df_st_JK_2007["Gender"]=="Male")["Total"])
t_JKm_2007
Out[1386]:
In [1387]:
t_JKfm_2007 = np.sum(df_st_JK_2007.where(df_st_JK_2007["Gender"]=="Female")["Total"])
t_JKfm_2007
Out[1387]:
In [1388]:
t_JHm_2007 = np.sum(df_st_JH_2007.where(df_st_JH_2007["Gender"]=="Male")["Total"])
t_JHm_2007
Out[1388]:
In [1389]:
t_JHfm_2007 = np.sum(df_st_JH_2007.where(df_st_JH_2007["Gender"]=="Female")["Total"])
t_JHfm_2007
Out[1389]:
In [1390]:
t_KNm_2007 = np.sum(df_st_KN_2007.where(df_st_KN_2007["Gender"]=="Male")["Total"])
t_KNm_2007
Out[1390]:
In [1391]:
t_KNfm_2007 = np.sum(df_st_KN_2007.where(df_st_KN_2007["Gender"]=="Female")["Total"])
t_KNfm_2007
Out[1391]:
In [1392]:
t_KERm_2007 = np.sum(df_st_KER_2007.where(df_st_KER_2007["Gender"]=="Male")["Total"])
t_KERm_2007
Out[1392]:
In [1393]:
t_KERfm_2007 = np.sum(df_st_KER_2007.where(df_st_KER_2007["Gender"]=="Female")["Total"])
t_KERfm_2007
Out[1393]:
In [1394]:
t_LDm_2007 = np.sum(df_st_LD_2007.where(df_st_LD_2007["Gender"]=="Male")["Total"])
t_LDm_2007
Out[1394]:
In [1395]:
t_LDfm_2007 = np.sum(df_st_LD_2007.where(df_st_LD_2007["Gender"]=="Female")["Total"])
t_LDfm_2007
Out[1395]:
In [1396]:
t_MPm_2007 = np.sum(df_st_MP_2007.where(df_st_MP_2007["Gender"]=="Male")["Total"])
t_MPm_2007
Out[1396]:
In [1397]:
t_MPfm_2007 = np.sum(df_st_MP_2007.where(df_st_MP_2007["Gender"]=="Female")["Total"])
t_MPfm_2007
Out[1397]:
In [1398]:
t_MHm_2007 = np.sum(df_st_MH_2007.where(df_st_MH_2007["Gender"]=="Male")["Total"])
t_MHm_2007
Out[1398]:
In [1399]:
t_MHfm_2007 = np.sum(df_st_MH_2007.where(df_st_MH_2007["Gender"]=="Female")["Total"])
t_MHfm_2007
Out[1399]:
In [1400]:
t_MNm_2007 = np.sum(df_st_MN_2007.where(df_st_MN_2007["Gender"]=="Male")["Total"])
t_MNm_2007
Out[1400]:
In [1401]:
t_MNfm_2007 = np.sum(df_st_MN_2007.where(df_st_MN_2007["Gender"]=="Female")["Total"])
t_MNfm_2007
Out[1401]:
In [1402]:
t_MGm_2007 = np.sum(df_st_MG_2007.where(df_st_MG_2007["Gender"]=="Male")["Total"])
t_MGm_2007
Out[1402]:
In [1403]:
t_MGfm_2007 = np.sum(df_st_MG_2007.where(df_st_MG_2007["Gender"]=="Female")["Total"])
t_MGfm_2007
Out[1403]:
In [1404]:
t_MZm_2007 = np.sum(df_st_MZ_2007.where(df_st_MZ_2007["Gender"]=="Male")["Total"])
t_MZm_2007
Out[1404]:
In [1405]:
t_MZfm_2007 = np.sum(df_st_MZ_2007.where(df_st_MZ_2007["Gender"]=="Female")["Total"])
t_MZfm_2007
Out[1405]:
In [1406]:
t_NGm_2007 = np.sum(df_st_NG_2007.where(df_st_NG_2007["Gender"]=="Male")["Total"])
t_NGm_2007
Out[1406]:
In [1407]:
t_NGfm_2007 = np.sum(df_st_NG_2007.where(df_st_NG_2007["Gender"]=="Female")["Total"])
t_MGfm_2007
Out[1407]:
In [1408]:
t_ODm_2007 = np.sum(df_st_OD_2007.where(df_st_OD_2007["Gender"]=="Male")["Total"])
t_ODm_2007
Out[1408]:
In [1409]:
t_ODfm_2007 = np.sum(df_st_OD_2007.where(df_st_OD_2007["Gender"]=="Female")["Total"])
t_ODfm_2007
Out[1409]:
In [1410]:
t_PDm_2007 = np.sum(df_st_PD_2007.where(df_st_PD_2007["Gender"]=="Male")["Total"])
t_PDm_2007
Out[1410]:
In [1411]:
t_PDfm_2007 = np.sum(df_st_PD_2007.where(df_st_PD_2007["Gender"]=="Female")["Total"])
t_PDfm_2007
Out[1411]:
In [1412]:
t_PBm_2007 = np.sum(df_st_PB_2007.where(df_st_PB_2007["Gender"]=="Male")["Total"])
t_PBm_2007
Out[1412]:
In [1413]:
t_PBfm_2007 = np.sum(df_st_PB_2007.where(df_st_PB_2007["Gender"]=="Female")["Total"])
t_PBfm_2007
Out[1413]:
In [1414]:
t_RJm_2007 = np.sum(df_st_RJ_2007.where(df_st_RJ_2007["Gender"]=="Male")["Total"])
t_RJm_2007
Out[1414]:
In [1415]:
t_RJfm_2007 = np.sum(df_st_RJ_2007.where(df_st_RJ_2007["Gender"]=="Female")["Total"])
t_RJfm_2007
Out[1415]:
In [1416]:
t_SMm_2007 = np.sum(df_st_SM_2007.where(df_st_SM_2007["Gender"]=="Male")["Total"])
t_SMm_2007
Out[1416]:
In [1417]:
t_SMfm_2007 = np.sum(df_st_SM_2007.where(df_st_SM_2007["Gender"]=="Female")["Total"])
t_SMfm_2007
Out[1417]:
In [1418]:
t_TNm_2007 = np.sum(df_st_TN_2007.where(df_st_TN_2007["Gender"]=="Male")["Total"])
t_TNm_2007
Out[1418]:
In [1419]:
t_TNfm_2007 = np.sum(df_st_TN_2007.where(df_st_TN_2007["Gender"]=="Female")["Total"])
t_TNfm_2007
Out[1419]:
In [1420]:
t_TPm_2007 = np.sum(df_st_TP_2007.where(df_st_TP_2007["Gender"]=="Male")["Total"])
t_TPm_2007
Out[1420]:
In [1421]:
t_TPfm_2007 = np.sum(df_st_TP_2007.where(df_st_TP_2007["Gender"]=="Female")["Total"])
t_TPfm_2007
Out[1421]:
In [1422]:
t_UPm_2007 = np.sum(df_st_UP_2007.where(df_st_UP_2007["Gender"]=="Male")["Total"])
t_UPm_2007
Out[1422]:
In [1423]:
t_UPfm_2007 = np.sum(df_st_UP_2007.where(df_st_UP_2007["Gender"]=="Female")["Total"])
t_UPfm_2007
Out[1423]:
In [1424]:
t_UKm_2007 = np.sum(df_st_UK_2007.where(df_st_UK_2007["Gender"]=="Male")["Total"])
t_UKm_2007
Out[1424]:
In [1425]:
t_UKfm_2007 = np.sum(df_st_UK_2007.where(df_st_UK_2007["Gender"]=="Female")["Total"])
t_UKfm_2007
Out[1425]:
In [1426]:
t_WBm_2007 = np.sum(df_st_WB_2007.where(df_st_WB_2007["Gender"]=="Male")["Total"])
t_WBm_2007
Out[1426]:
In [1427]:
t_WBfm_2007 = np.sum(df_st_WB_2007.where(df_st_WB_2007["Gender"]=="Female")["Total"])
t_WBfm_2007
Out[1427]:
In [1428]:
l_2007=list(df_2007.loc[:,"Type"].unique())
l_2007.sort()
print(l_2007,end=" ")
In [1429]:
cause_l_2007=[]
for i in l_2007:
rough= np.sum(df_2007.where(df_2007["Type"]==i)["Total"])
cause_l_2007.append(rough)
print(cause_l_2007,end=" ")
In [1430]:
x1_2007 = set(df_2007.loc[:,"State"])
x1_2007
len(x1_2007)
Out[1430]:
In [1431]:
cause_df_2007 = pd.DataFrame(cause_l_2007,l_2007)
cause_df_2007.reset_index()
Out[1431]:
In [1432]:
cause_df_2007.plot.bar(stacked=True,figsize=(50,20))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("Causes",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths due to Different Causes in 2007",fontsize=55)
plt.legend(["Death Count"], prop={"size":30})
Out[1432]:
In [1433]:
s_2007=list(df_2007.loc[:,"State"].unique())
s_2007.sort()
print(s_2007,end=" ")
In [1434]:
state_s_2007 = [s7_19,s7_20,s7_21,s7_22,s7_23,s7_24,s7_25,s7_26,s7_27,s7_28,s7_29,s7_30,s7_31,s7_32,s7_33,s7_34,
s7_35,s7_36,s7_37,s7_38,s7_39,s7_40,s7_41,s7_42,s7_43,s7_44,s7_45,s7_46,s7_47,s7_48,s7_49,s7_50,
s7_51,s7_52,s7_53]
print(state_s_2007,end=" ")
In [1435]:
state_m_2007=[t_ANm_2007, t_APm_2007, t_ARPm_2007, t_ASm_2007, t_BHm_2007, t_CHm_2007, t_CTm_2007, t_DNm_2007,
t_DDm_2007, t_DLm_2007, t_GOAm_2007, t_GJm_2007, t_HRm_2007, t_HPm_2007,t_JKm_2007, t_JHm_2007,
t_KNm_2007, t_KERm_2007, t_LDm_2007,t_MPm_2007,t_MHm_2007,t_MNm_2007,t_MGm_2007,t_MZm_2007,
t_NGm_2007, t_ODm_2007, t_PDm_2007,t_PBm_2007,t_RJm_2007,t_SMm_2007,t_TNm_2007,t_TPm_2007,
t_UPm_2007, t_UKm_2007, t_WBm_2007]
print(state_m_2007,end=" ")
In [1436]:
state_fm_2007=[t_ANfm_2007, t_APfm_2007, t_ARPfm_2007, t_ASfm_2007, t_BHfm_2007, t_CHfm_2007, t_CTfm_2007, t_DNfm_2007,
t_DDfm_2007, t_DLfm_2007, t_GOAfm_2007, t_GJfm_2007, t_HRfm_2007, t_HPfm_2007,t_JKfm_2007, t_JHfm_2007,
t_KNfm_2007, t_KERfm_2007, t_LDfm_2007,t_MPfm_2007,t_MHfm_2007,t_MNfm_2007,t_MGfm_2007,t_MZfm_2007,
t_NGfm_2007, t_ODfm_2007, t_PDfm_2007,t_PBfm_2007,t_RJfm_2007,t_SMfm_2007,t_TNfm_2007,t_TPfm_2007,
t_UPfm_2007, t_UKfm_2007, t_WBfm_2007]
print(state_fm_2007,end=" ")
In [1437]:
df_state_2007 = {
"State":['A & N ISLANDS', 'ANDHRA PRADESH', 'ARUNACHAL PRADESH', 'ASSAM', 'BIHAR', 'CHANDIGARH', 'CHHATTISGARH',
'D & N HAVELI', 'DAMAN & DIU', 'DELHI (UT)', 'GOA', 'GUJARAT', 'HARYANA', 'HIMACHAL PRADESH', 'JAMMU & KASHMIR',
'JHARKHAND', 'KARNATAKA', 'KERALA', 'LAKSHADWEEP', 'MADHYA PRADESH', 'MAHARASHTRA', 'MANIPUR', 'MEGHALAYA',
'MIZORAM', 'NAGALAND', 'ODISHA', 'PUDUCHERRY', 'PUNJAB', 'RAJASTHAN', 'SIKKIM', 'TAMIL NADU', 'TRIPURA',
'UTTAR PRADESH', 'UTTARAKHAND', 'WEST BENGAL'],
"Total":[s7_19,s7_20,s7_21,s7_22,s7_23,s7_24,s7_25,s7_26,s7_27,s7_28,s7_29,s7_30,s7_31,s7_32,s7_33,s7_34,
s7_35,s7_36,s7_37,s7_38,s7_39,s7_40,s7_41,s7_42,s7_43,s7_44,s7_45,s7_46,s7_47,s7_48,s7_49,s7_50,
s7_51,s7_52,s7_53],
"Male":[t_ANm_2007, t_APm_2007, t_ARPm_2007, t_ASm_2007, t_BHm_2007, t_CHm_2007, t_CTm_2007, t_DNm_2007,
t_DDm_2007, t_DLm_2007, t_GOAm_2007, t_GJm_2007, t_HRm_2007, t_HPm_2007,t_JKm_2007, t_JHm_2007,
t_KNm_2007, t_KERm_2007, t_LDm_2007,t_MPm_2007,t_MHm_2007,t_MNm_2007,t_MGm_2007,t_MZm_2007,
t_NGm_2007, t_ODm_2007, t_PDm_2007,t_PBm_2007,t_RJm_2007,t_SMm_2007,t_TNm_2007,t_TPm_2007,
t_UPm_2007, t_UKm_2007, t_WBm_2007],
"Female":[t_ANfm_2007, t_APfm_2007, t_ARPfm_2007, t_ASfm_2007, t_BHfm_2007, t_CHfm_2007, t_CTfm_2007, t_DNfm_2007,
t_DDfm_2007, t_DLfm_2007, t_GOAfm_2007, t_GJfm_2007, t_HRfm_2007, t_HPfm_2007,t_JKfm_2007, t_JHfm_2007,
t_KNfm_2007, t_KERfm_2007, t_LDfm_2007,t_MPfm_2007,t_MHfm_2007,t_MNfm_2007,t_MGfm_2007,t_MZfm_2007,
t_NGfm_2007, t_ODfm_2007, t_PDfm_2007,t_PBfm_2007,t_RJfm_2007,t_SMfm_2007,t_TNfm_2007,t_TPfm_2007,
t_UPfm_2007, t_UKfm_2007, t_WBfm_2007]
}
df_All_st_2007 = pd.DataFrame(df_state_2007)
df_All_st_2007
Out[1437]:
In [1438]:
pivot_2007 = pd.pivot_table(df_All_st_2007, values=None, index='State', columns=None,
fill_value=None, margins=False, dropna=True, margins_name='All', observed=False)
pivot_2007
Out[1438]:
In [1439]:
pivot_2007.plot(kind='bar',figsize=(60,30))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("States",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths of Males And Females In Different State",fontsize=55)
plt.legend(["Female","Male","Total"], prop={"size":30})
plt.show
Out[1439]:
In [1440]:
cause_l_percent_2007=[]
for i in cause_l_2007:
p=i/np.sum(cause_l_2007)
avg_2007_cause=p*100
cause_l_percent_2007.append(avg_2007_cause)
print(cause_l_percent_2007,end=" ")
In [1441]:
np.max(cause_l_percent_2007) #Married
Out[1441]:
In [1442]:
np.min(cause_l_percent_2007) #Ideological Causes/Hero Worshipping
Out[1442]:
In [1443]:
state_s_2007_percent=[]
tot=np.sum(state_s_2007)
for i in state_s_2007:
xy=i/tot
avg_state_2007=xy*100
state_s_2007_percent.append(avg_state_2007)
print(state_s_2007_percent,end=" ")
In [1444]:
np.max(state_s_2007_percent) #Maharashtra
Out[1444]:
In [1445]:
np.min(state_s_2007_percent) #Lakshdweep
Out[1445]:
In [1446]:
l_age_grp_2007=[s7_4,s7_5,s7_6,s7_7,s7_8,s7_54]
l_age_grp_2007
Out[1446]:
In [1447]:
l_age_grp_2007_percent=[]
for i in l_age_grp_2007:
yz=i/s7_1
avg_2007_age_grp=yz*100
l_age_grp_2007_percent.append(avg_2007_age_grp)
l_age_grp_2007_percent
Out[1447]:
In [1448]:
np.max(l_age_grp_2007_percent) #0-100+
Out[1448]:
In [1449]:
np.min(l_age_grp_2007_percent) #0-14
Out[1449]:
In [1450]:
total_male_percent_2007= (s7_2/s7_1)*100
total_male_percent_2007
Out[1450]:
In [1451]:
total_female_percent_2007= (s7_3/s7_1)*100
total_female_percent_2007
Out[1451]:
In [ ]:
2008¶
In [1452]:
df_2008 = suicide_df.loc[(suicide_df["Year"]==2008)]
df_2008
Out[1452]:
In [1453]:
t2008_dcount = df_2008.loc[:,"Total"]
s8_1 = np.sum(t2008_dcount)
s8_1
Out[1453]:
In [1454]:
male_2008 = df_2008.loc[(df_2008["Gender"]=="Male")]
male_2008
Out[1454]:
In [1455]:
m2008_count = male_2008.loc[:,"Total"]
s8_2 = np.sum(m2008_count)
s8_2
Out[1455]:
In [1456]:
female_2008 = df_2008.loc[(df_2008["Gender"]=="Female")]
female_2008
Out[1456]:
In [1457]:
fm2008_count = female_2008.loc[:,"Total"]
s8_3 = np.sum(fm2008_count)
s8_3
Out[1457]:
In [1458]:
df_age_d014_2008 = df_2008.loc[(df_2008["Age_group"]=="0-14")]
df_age_d014_2008
Out[1458]:
In [1459]:
t_014_2008 = df_age_d014_2008.loc[:,"Total"]
s8_4 = np.sum(t_014_2008)
s8_4
Out[1459]:
In [1460]:
df_age_d1529_2008 = df_2008.loc[(df_2008["Age_group"]=="15-29")]
df_age_d1529_2008
Out[1460]:
In [1461]:
t_1529_2008 = df_age_d1529_2008.loc[:,"Total"]
s8_5 = np.sum(t_1529_2008)
s8_5
Out[1461]:
In [1462]:
df_age_d3044_2008 = df_2008.loc[(df_2008["Age_group"]=="30-44")]
df_age_d3044_2008
Out[1462]:
In [1463]:
t_3044_2008 = df_age_d3044_2008.loc[:,"Total"]
s8_6 = np.sum(t_3044_2008)
s8_6
Out[1463]:
In [1464]:
df_age_d4559_2008 = df_2008.loc[(df_2008["Age_group"]=="45-59")]
df_age_d4559_2008
Out[1464]:
In [1465]:
t_4559_2008 = df_age_d4559_2008.loc[:,"Total"]
s8_7 = np.sum(t_4559_2008)
s8_7
Out[1465]:
In [1466]:
df_age_d60_2008 = df_2008.loc[(df_2008["Age_group"]=="60+")]
df_age_d60_2008
Out[1466]:
In [1467]:
t_60_2008 = df_age_d60_2008.loc[:,"Total"]
s8_8 = np.sum(t_60_2008)
s8_8
Out[1467]:
In [1468]:
df_age_d0100_2008 = df_2008.loc[(df_2008["Age_group"]=="0-100+")]
df_age_d0100_2008
Out[1468]:
In [1469]:
t_0100_2008 = df_age_d0100_2008.loc[:,"Total"]
s8_54 = np.sum(t_0100_2008)
s8_54
Out[1469]:
In [1470]:
df_014m_2008 = df_age_d014_2008.loc[(df_age_d014_2008["Gender"]=="Male")]
df_014m_2008
Out[1470]:
In [1471]:
t_014m_2008 = df_014m_2008.loc[:,"Total"]
s8_9 = np.sum(t_014m_2008)
s8_9
Out[1471]:
In [1472]:
df_014fm_2008 = df_age_d014_2008.loc[(df_age_d014_2008["Gender"]=="Female")]
df_014fm_2008
Out[1472]:
In [1473]:
t_014fm_2008 = df_014fm_2008.loc[:,"Total"]
s8_10 = np.sum(t_014fm_2008)
s8_10
Out[1473]:
In [1474]:
df_1529m_2008 = df_age_d1529_2008.loc[(df_age_d1529_2008["Gender"]=="Male")]
df_1529m_2008
Out[1474]:
In [1475]:
t_1529m_2008 = df_1529m_2008.loc[:,"Total"]
s8_11 = np.sum(t_1529m_2008)
s8_11
Out[1475]:
In [1476]:
df_1529fm_2008 = df_age_d1529_2008.loc[(df_age_d1529_2008["Gender"]=="Female")]
df_1529fm_2008
Out[1476]:
In [1477]:
t_1529fm_2008 = df_1529fm_2008.loc[:,"Total"]
s8_12 = np.sum(t_1529fm_2008)
s8_12
Out[1477]:
In [1478]:
df_3044m_2008 = df_age_d3044_2008.loc[(df_age_d3044_2008["Gender"]=="Male")]
df_3044m_2008
Out[1478]:
In [1479]:
t_3044m_2008 = df_3044m_2008.loc[:,"Total"]
s8_13 = np.sum(t_3044m_2008)
s8_13
Out[1479]:
In [1480]:
df_3044fm_2008 = df_age_d3044_2008.loc[(df_age_d3044_2008["Gender"]=="Female")]
df_3044fm_2008
Out[1480]:
In [1481]:
t_3044fm_2008 = df_3044fm_2008.loc[:,"Total"]
s8_14 = np.sum(t_3044fm_2008)
s8_14
Out[1481]:
In [1482]:
df_4559m_2008 = df_age_d4559_2008.loc[(df_age_d4559_2008["Gender"]=="Male")]
df_4559m_2008
Out[1482]:
In [1483]:
t_4559m_2008 = df_4559m_2008.loc[:,"Total"]
s8_15 = np.sum(t_4559m_2008)
s8_15
Out[1483]:
In [1484]:
df_4559fm_2008 = df_age_d4559_2008.loc[(df_age_d4559_2008["Gender"]=="Female")]
df_4559fm_2008
Out[1484]:
In [1485]:
t_4559fm_2008 = df_4559fm_2008.loc[:,"Total"]
s8_16 = np.sum(t_4559fm_2008)
s8_16
Out[1485]:
In [1486]:
df_60m_2008 = df_age_d60_2008.loc[(df_age_d60_2008["Gender"]=="Male")]
df_60m_2008
Out[1486]:
In [1487]:
t_60m_2008 = df_60m_2008.loc[:,"Total"]
s8_17 = np.sum(t_60m_2008)
s8_17
Out[1487]:
In [1488]:
df_60fm_2008 = df_age_d60_2008.loc[(df_age_d60_2008["Gender"]=="Female")]
df_60fm_2008
Out[1488]:
In [1489]:
t_60fm_2008 = df_60fm_2008.loc[:,"Total"]
s8_18 = np.sum(t_60fm_2008)
s8_18
Out[1489]:
In [1490]:
df_0100fm_2008 = df_age_d0100_2008.loc[(df_age_d0100_2008["Gender"]=="Female")]
df_0100fm_2008
Out[1490]:
In [1491]:
t_0100fm_2008 = df_0100fm_2008.loc[:,"Total"]
s8_55 = np.sum(t_0100fm_2008)
s8_55
Out[1491]:
In [1492]:
df_0100m_2008 = df_age_d0100_2008.loc[(df_age_d0100_2008["Gender"]=="Male")]
df_0100m_2008
Out[1492]:
In [1493]:
t_0100m_2008 = df_0100m_2008.loc[:,"Total"]
s8_56 = np.sum(t_0100m_2008)
s8_56
Out[1493]:
In [1494]:
df_st_AN_2008 = df_2008.loc[(df_2008["State"]=="A & N ISLANDS")]
df_st_AN_2008
Out[1494]:
In [1495]:
t_AN_2008 = df_st_AN_2008.loc[:,"Total"]
s8_19 = np.sum(t_AN_2008)
s8_19
Out[1495]:
In [1496]:
df_st_AP_2008 = df_2008.loc[(df_2008["State"]=="ANDHRA PRADESH")]
df_st_AP_2008
Out[1496]:
In [1497]:
t_AP_2008 = df_st_AP_2008.loc[:,"Total"]
s8_20 = np.sum(t_AP_2008)
s8_20
Out[1497]:
In [1498]:
df_st_ARP_2008 = df_2008.loc[(df_2008["State"]=="ARUNACHAL PRADESH")]
df_st_ARP_2008
Out[1498]:
In [1499]:
t_ARP_2008 = df_st_ARP_2008.loc[:,"Total"]
s8_21 = np.sum(t_ARP_2008)
s8_21
Out[1499]:
In [1500]:
df_st_AS_2008 = df_2008.loc[(df_2008["State"]=="ASSAM")]
df_st_AS_2008
Out[1500]:
In [1501]:
t_AS_2008 = df_st_AS_2008.loc[:,"Total"]
s8_22 = np.sum(t_AS_2008)
s8_22
Out[1501]:
In [1502]:
df_st_BH_2008 = df_2008.loc[(df_2008["State"]=="BIHAR")]
df_st_BH_2008
Out[1502]:
In [1503]:
t_BH_2008 = df_st_BH_2008.loc[:,"Total"]
s8_23 = np.sum(t_BH_2008)
s8_23
Out[1503]:
In [1504]:
df_st_CH_2008 = df_2008.loc[(df_2008["State"]=="CHANDIGARH")]
df_st_CH_2008
Out[1504]:
In [1505]:
t_CH_2008 = df_st_CH_2008.loc[:,"Total"]
s8_24 = np.sum(t_CH_2008)
s8_24
Out[1505]:
In [1506]:
df_st_CT_2008 = df_2008.loc[(df_2008["State"]=="CHHATTISGARH")]
df_st_CT_2008
Out[1506]:
In [1507]:
t_CT_2008 = df_st_CT_2008.loc[:,"Total"]
s8_25 = np.sum(t_CT_2008)
s8_25
Out[1507]:
In [1508]:
df_st_DN_2008 = df_2008.loc[(df_2008["State"]=="D & N HAVELI")]
df_st_DN_2008
Out[1508]:
In [1509]:
t_DN_2008 = df_st_DN_2008.loc[:,"Total"]
s8_26 = np.sum(t_DN_2008)
s8_26
Out[1509]:
In [1510]:
df_st_DD_2008 = df_2008.loc[(df_2008["State"]=="DAMAN & DIU")]
df_st_DD_2008
Out[1510]:
In [1511]:
t_DD_2008 = df_st_DD_2008.loc[:,"Total"]
s8_27 = np.sum(t_DD_2008)
s8_27
Out[1511]:
In [1512]:
df_st_DL_2008 = df_2008.loc[(df_2008["State"]=="DELHI (UT)")]
df_st_DL_2008
Out[1512]:
In [1513]:
t_DL_2008 = df_st_DL_2008.loc[:,"Total"]
s8_28 = np.sum(t_DL_2008)
s8_28
Out[1513]:
In [1514]:
df_st_GOA_2008 = df_2008.loc[(df_2008["State"]=="GOA")]
df_st_GOA_2008
Out[1514]:
In [1515]:
t_GOA_2008 = df_st_GOA_2008.loc[:,"Total"]
s8_29 = np.sum(t_GOA_2008)
s8_29
Out[1515]:
In [1516]:
df_st_GJ_2008 = df_2008.loc[(df_2008["State"]=="GUJARAT")]
df_st_GJ_2008
Out[1516]:
In [1517]:
t_GJ_2008 = df_st_GJ_2008.loc[:,"Total"]
s8_30 = np.sum(t_GJ_2008)
s8_30
Out[1517]:
In [1518]:
df_st_HR_2008 = df_2008.loc[(df_2008["State"]=="HARYANA")]
df_st_HR_2008
Out[1518]:
In [1519]:
t_HR_2008 = df_st_HR_2008.loc[:,"Total"]
s8_31 = np.sum(t_HR_2008)
s8_31
Out[1519]:
In [1520]:
df_st_HP_2008 = df_2008.loc[(df_2008["State"]=="HIMACHAL PRADESH")]
df_st_HP_2008
Out[1520]:
In [1521]:
t_HP_2008 = df_st_HP_2008.loc[:,"Total"]
s8_32 = np.sum(t_HP_2008)
s8_32
Out[1521]:
In [1522]:
df_st_JK_2008 = df_2008.loc[(df_2008["State"]=="JAMMU & KASHMIR")]
df_st_JK_2008
Out[1522]:
In [1523]:
t_JK_2008 = df_st_JK_2008.loc[:,"Total"]
s8_33 = np.sum(t_JK_2008)
s8_33
Out[1523]:
In [1524]:
df_st_JH_2008 = df_2008.loc[(df_2008["State"]=="JHARKHAND")]
df_st_JH_2008
Out[1524]:
In [1525]:
t_JH_2008 = df_st_JH_2008.loc[:,"Total"]
s8_34 = np.sum(t_JH_2008)
s8_34
Out[1525]:
In [1526]:
df_st_KN_2008 = df_2008.loc[(df_2008["State"]=="KARNATAKA")]
df_st_KN_2008
Out[1526]:
In [1527]:
t_KN_2008 = df_st_KN_2008.loc[:,"Total"]
s8_35 = np.sum(t_KN_2008)
s8_35
Out[1527]:
In [1528]:
df_st_KER_2008 = df_2008.loc[(df_2008["State"]=="KERALA")]
df_st_KER_2008
Out[1528]:
In [1529]:
t_KER_2008 = df_st_KER_2008.loc[:,"Total"]
s8_36 = np.sum(t_KER_2008)
s8_36
Out[1529]:
In [1530]:
df_st_LD_2008 = df_2008.loc[(df_2008["State"]=="LAKSHADWEEP")]
df_st_LD_2008
Out[1530]:
In [1531]:
t_LD_2008 = df_st_LD_2008.loc[:,"Total"]
s8_37 = np.sum(t_LD_2008)
s8_37
Out[1531]:
In [1532]:
df_st_MP_2008 = df_2008.loc[(df_2008["State"]=="MADHYA PRADESH")]
df_st_MP_2008
Out[1532]:
In [1533]:
t_MP_2008 = df_st_MP_2008.loc[:,"Total"]
s8_38 = np.sum(t_MP_2008)
s8_38
Out[1533]:
In [1534]:
df_st_MH_2008 = df_2008.loc[(df_2008["State"]=="MAHARASHTRA")]
df_st_MH_2008
Out[1534]:
In [1535]:
t_MH_2008 = df_st_MH_2008.loc[:,"Total"]
s8_39 = np.sum(t_MH_2008)
s8_39
Out[1535]:
In [1536]:
df_st_MN_2008 = df_2008.loc[(df_2008["State"]=="MANIPUR")]
df_st_MN_2008
Out[1536]:
In [1537]:
t_MN_2008 = df_st_MN_2008.loc[:,"Total"]
s8_40 = np.sum(t_MN_2008)
s8_40
Out[1537]:
In [1538]:
df_st_MG_2008 = df_2008.loc[(df_2008["State"]=="MEGHALAYA")]
df_st_MG_2008
Out[1538]:
In [1539]:
t_MG_2008 = df_st_MG_2008.loc[:,"Total"]
s8_41 = np.sum(t_MG_2008)
s8_41
Out[1539]:
In [1540]:
df_st_MZ_2008 = df_2008.loc[(df_2008["State"]=="MIZORAM")]
df_st_MZ_2008
Out[1540]:
In [1541]:
t_MN_2008 = df_st_MN_2008.loc[:,"Total"]
s8_42 = np.sum(t_MN_2008)
s8_42
Out[1541]:
In [1542]:
df_st_NG_2008 = df_2008.loc[(df_2008["State"]=="NAGALAND")]
df_st_NG_2008
Out[1542]:
In [1543]:
t_NG_2008 = df_st_NG_2008.loc[:,"Total"]
s8_43 = np.sum(t_NG_2008)
s8_43
Out[1543]:
In [1544]:
df_st_OD_2008 = df_2008.loc[(df_2008["State"]=="ODISHA")]
df_st_OD_2008
Out[1544]:
In [1545]:
t_OD_2008 = df_st_OD_2008.loc[:,"Total"]
s8_44 = np.sum(t_OD_2008)
s8_44
Out[1545]:
In [1546]:
df_st_PD_2008 = df_2008.loc[(df_2008["State"]=="PUDUCHERRY")]
df_st_PD_2008
Out[1546]:
In [1547]:
t_PD_2008 = df_st_PD_2008.loc[:,"Total"]
s8_45 = np.sum(t_PD_2008)
s8_45
Out[1547]:
In [1548]:
df_st_PB_2008 = df_2008.loc[(df_2008["State"]=="PUNJAB")]
df_st_PB_2008
Out[1548]:
In [1549]:
t_PB_2008 = df_st_PB_2008.loc[:,"Total"]
s8_46 = np.sum(t_PB_2008)
s8_46
Out[1549]:
In [1550]:
df_st_RJ_2008 = df_2008.loc[(df_2008["State"]=="RAJASTHAN")]
df_st_RJ_2008
Out[1550]:
In [1551]:
t_RJ_2008 = df_st_RJ_2008.loc[:,"Total"]
s8_47 = np.sum(t_RJ_2008)
s8_47
Out[1551]:
In [1552]:
df_st_SM_2008 = df_2008.loc[(df_2008["State"]=="SIKKIM")]
df_st_SM_2008
Out[1552]:
In [1553]:
t_SM_2008 = df_st_SM_2008.loc[:,"Total"]
s8_48 = np.sum(t_SM_2008)
s8_48
Out[1553]:
In [1554]:
df_st_TN_2008 = df_2008.loc[(df_2008["State"]=="TAMIL NADU")]
df_st_TN_2008
Out[1554]:
In [1555]:
t_TN_2008 = df_st_TN_2008.loc[:,"Total"]
s8_49 = np.sum(t_TN_2008)
s8_49
Out[1555]:
In [1556]:
df_st_TP_2008 = df_2008.loc[(df_2008["State"]=="TRIPURA")]
df_st_TP_2008
Out[1556]:
In [1557]:
t_TP_2008 = df_st_TP_2008.loc[:,"Total"]
s8_50 = np.sum(t_TP_2008)
s8_50
Out[1557]:
In [1558]:
df_st_UP_2008 = df_2008.loc[(df_2008["State"]=="UTTAR PRADESH")]
df_st_UP_2008
Out[1558]:
In [1559]:
t_UP_2008 = df_st_UP_2008.loc[:,"Total"]
s8_51 = np.sum(t_UP_2008)
s8_51
Out[1559]:
In [1560]:
df_st_UK_2008 = df_2008.loc[(df_2008["State"]=="UTTARAKHAND")]
df_st_UK_2008
Out[1560]:
In [1561]:
t_UK_2008 = df_st_UK_2008.loc[:,"Total"]
s8_52 = np.sum(t_UK_2008)
s8_52
Out[1561]:
In [1562]:
df_st_WB_2008 = df_2008.loc[(df_2008["State"]=="WEST BENGAL")]
df_st_WB_2008
Out[1562]:
In [1563]:
t_WB_2008 = df_st_WB_2008.loc[:,"Total"]
s8_53 = np.sum(t_WB_2008)
s8_53
Out[1563]:
In [1564]:
t_ANm_2008 = np.sum(df_st_AN_2008.where(df_st_AN_2008["Gender"]=="Male")["Total"])
t_ANm_2008
Out[1564]:
In [1565]:
t_ANfm_2008 = np.sum(df_st_AN_2008.where(df_st_AN_2008["Gender"]=="Female")["Total"])
t_ANfm_2008
Out[1565]:
In [1566]:
t_APm_2008 = np.sum(df_st_AP_2008.where(df_st_AP_2008["Gender"]=="Male")["Total"])
t_APm_2008
Out[1566]:
In [1567]:
t_APfm_2008 = np.sum(df_st_AP_2008.where(df_st_AP_2008["Gender"]=="Female")["Total"])
t_APfm_2008
Out[1567]:
In [1568]:
t_ARPm_2008 = np.sum(df_st_ARP_2008.where(df_st_ARP_2008["Gender"]=="Male")["Total"])
t_ARPm_2008
Out[1568]:
In [1569]:
t_ARPfm_2008 = np.sum(df_st_ARP_2008.where(df_st_ARP_2008["Gender"]=="Female")["Total"])
t_ARPfm_2008
Out[1569]:
In [1570]:
t_ASm_2008 = np.sum(df_st_AS_2008.where(df_st_AS_2008["Gender"]=="Male")["Total"])
t_ASm_2008
Out[1570]:
In [1571]:
t_ASfm_2008 = np.sum(df_st_AS_2008.where(df_st_AS_2008["Gender"]=="Female")["Total"])
t_ASfm_2008
Out[1571]:
In [1572]:
t_BHm_2008 = np.sum(df_st_BH_2008.where(df_st_BH_2008["Gender"]=="Male")["Total"])
t_BHm_2008
Out[1572]:
In [1573]:
t_BHfm_2008 = np.sum(df_st_BH_2008.where(df_st_BH_2008["Gender"]=="Female")["Total"])
t_BHfm_2008
Out[1573]:
In [1574]:
t_CHm_2008 = np.sum(df_st_CH_2008.where(df_st_CH_2008["Gender"]=="Male")["Total"])
t_CHm_2008
Out[1574]:
In [1575]:
t_CHfm_2008 = np.sum(df_st_CH_2008.where(df_st_CH_2008["Gender"]=="Female")["Total"])
t_CHfm_2008
Out[1575]:
In [1576]:
t_CTm_2008 = np.sum(df_st_CT_2008.where(df_st_CT_2008["Gender"]=="Male")["Total"])
t_CTm_2008
Out[1576]:
In [1577]:
t_CTfm_2008 = np.sum(df_st_CT_2008.where(df_st_CT_2008["Gender"]=="Female")["Total"])
t_CTfm_2008
Out[1577]:
In [1578]:
t_DNm_2008 = np.sum(df_st_DN_2008.where(df_st_DN_2008["Gender"]=="Male")["Total"])
t_DNm_2008
Out[1578]:
In [1579]:
t_DNfm_2008 = np.sum(df_st_DN_2008.where(df_st_DN_2008["Gender"]=="Female")["Total"])
t_DNfm_2008
Out[1579]:
In [1580]:
t_DDm_2008 = np.sum(df_st_DD_2008.where(df_st_DD_2008["Gender"]=="Male")["Total"])
t_DDm_2008
Out[1580]:
In [1581]:
t_DDfm_2008 = np.sum(df_st_DD_2008.where(df_st_DD_2008["Gender"]=="Female")["Total"])
t_DDfm_2008
Out[1581]:
In [1582]:
t_DLm_2008 = np.sum(df_st_DL_2008.where(df_st_DL_2008["Gender"]=="Male")["Total"])
t_DLm_2008
Out[1582]:
In [1583]:
t_DLfm_2008 = np.sum(df_st_DL_2008.where(df_st_DL_2008["Gender"]=="Female")["Total"])
t_DLfm_2008
Out[1583]:
In [1584]:
t_GOAm_2008 = np.sum(df_st_GOA_2008.where(df_st_GOA_2008["Gender"]=="Male")["Total"])
t_GOAm_2008
Out[1584]:
In [1585]:
t_GOAfm_2008 = np.sum(df_st_GOA_2008.where(df_st_GOA_2008["Gender"]=="Female")["Total"])
t_GOAfm_2008
Out[1585]:
In [1586]:
t_GJm_2008 = np.sum(df_st_GJ_2008.where(df_st_GJ_2008["Gender"]=="Male")["Total"])
t_GJm_2008
Out[1586]:
In [1587]:
t_GJfm_2008 = np.sum(df_st_GJ_2008.where(df_st_GJ_2008["Gender"]=="Female")["Total"])
t_GJfm_2008
Out[1587]:
In [1588]:
t_HRm_2008 = np.sum(df_st_HR_2008.where(df_st_HR_2008["Gender"]=="Male")["Total"])
t_HRm_2008
Out[1588]:
In [1589]:
t_HRfm_2008 = np.sum(df_st_HR_2008.where(df_st_HR_2008["Gender"]=="Female")["Total"])
t_HRfm_2008
Out[1589]:
In [1590]:
t_HPm_2008 = np.sum(df_st_HP_2008.where(df_st_HP_2008["Gender"]=="Male")["Total"])
t_HPm_2008
Out[1590]:
In [1591]:
t_HPfm_2008 = np.sum(df_st_HP_2008.where(df_st_HP_2008["Gender"]=="Female")["Total"])
t_HPfm_2008
Out[1591]:
In [1592]:
t_JKm_2008 = np.sum(df_st_JK_2008.where(df_st_JK_2008["Gender"]=="Male")["Total"])
t_JKm_2008
Out[1592]:
In [1593]:
t_JKfm_2008 = np.sum(df_st_JK_2008.where(df_st_JK_2008["Gender"]=="Female")["Total"])
t_JKfm_2008
Out[1593]:
In [1594]:
t_JHm_2008 = np.sum(df_st_JH_2008.where(df_st_JH_2008["Gender"]=="Male")["Total"])
t_JHm_2008
Out[1594]:
In [1595]:
t_JHfm_2008 = np.sum(df_st_JH_2008.where(df_st_JH_2008["Gender"]=="Female")["Total"])
t_JHfm_2008
Out[1595]:
In [1596]:
t_KNm_2008 = np.sum(df_st_KN_2008.where(df_st_KN_2008["Gender"]=="Male")["Total"])
t_KNm_2008
Out[1596]:
In [1597]:
t_KNfm_2008 = np.sum(df_st_KN_2008.where(df_st_KN_2008["Gender"]=="Female")["Total"])
t_KNfm_2008
Out[1597]:
In [1598]:
t_KERm_2008 = np.sum(df_st_KER_2008.where(df_st_KER_2008["Gender"]=="Male")["Total"])
t_KERm_2008
Out[1598]:
In [1599]:
t_KERfm_2008 = np.sum(df_st_KER_2008.where(df_st_KER_2008["Gender"]=="Female")["Total"])
t_KERfm_2008
Out[1599]:
In [1600]:
t_LDm_2008 = np.sum(df_st_LD_2008.where(df_st_LD_2008["Gender"]=="Male")["Total"])
t_LDm_2008
Out[1600]:
In [1601]:
t_LDfm_2008 = np.sum(df_st_LD_2008.where(df_st_LD_2008["Gender"]=="Female")["Total"])
t_LDfm_2008
Out[1601]:
In [1602]:
t_MPm_2008 = np.sum(df_st_MP_2008.where(df_st_MP_2008["Gender"]=="Male")["Total"])
t_MPm_2008
Out[1602]:
In [1603]:
t_MPfm_2008 = np.sum(df_st_MP_2008.where(df_st_MP_2008["Gender"]=="Female")["Total"])
t_MPfm_2008
Out[1603]:
In [1604]:
t_MHm_2008 = np.sum(df_st_MH_2008.where(df_st_MH_2008["Gender"]=="Male")["Total"])
t_MHm_2008
Out[1604]:
In [1605]:
t_MHfm_2008 = np.sum(df_st_MH_2008.where(df_st_MH_2008["Gender"]=="Female")["Total"])
t_MHfm_2008
Out[1605]:
In [1606]:
t_MNm_2008 = np.sum(df_st_MN_2008.where(df_st_MN_2008["Gender"]=="Male")["Total"])
t_MNm_2008
Out[1606]:
In [1607]:
t_MNfm_2008 = np.sum(df_st_MN_2008.where(df_st_MN_2008["Gender"]=="Female")["Total"])
t_MNfm_2008
Out[1607]:
In [1608]:
t_MGm_2008 = np.sum(df_st_MG_2008.where(df_st_MG_2008["Gender"]=="Male")["Total"])
t_MGm_2008
Out[1608]:
In [1609]:
t_MGfm_2008 = np.sum(df_st_MG_2008.where(df_st_MG_2008["Gender"]=="Female")["Total"])
t_MGfm_2008
Out[1609]:
In [1610]:
t_MZm_2008 = np.sum(df_st_MZ_2008.where(df_st_MZ_2008["Gender"]=="Male")["Total"])
t_MZm_2008
Out[1610]:
In [1611]:
t_MZfm_2008 = np.sum(df_st_MZ_2008.where(df_st_MZ_2008["Gender"]=="Female")["Total"])
t_MZfm_2008
Out[1611]:
In [1612]:
t_NGm_2008 = np.sum(df_st_NG_2008.where(df_st_NG_2008["Gender"]=="Male")["Total"])
t_NGm_2008
Out[1612]:
In [1613]:
t_NGfm_2008 = np.sum(df_st_NG_2008.where(df_st_NG_2008["Gender"]=="Female")["Total"])
t_MGfm_2008
Out[1613]:
In [1614]:
t_ODm_2008 = np.sum(df_st_OD_2008.where(df_st_OD_2008["Gender"]=="Male")["Total"])
t_ODm_2008
Out[1614]:
In [1615]:
t_ODfm_2008 = np.sum(df_st_OD_2008.where(df_st_OD_2008["Gender"]=="Female")["Total"])
t_ODfm_2008
Out[1615]:
In [1616]:
t_PDm_2008 = np.sum(df_st_PD_2008.where(df_st_PD_2008["Gender"]=="Male")["Total"])
t_PDm_2008
Out[1616]:
In [1617]:
t_PDfm_2008 = np.sum(df_st_PD_2008.where(df_st_PD_2008["Gender"]=="Female")["Total"])
t_PDfm_2008
Out[1617]:
In [1618]:
t_PBm_2008 = np.sum(df_st_PB_2008.where(df_st_PB_2008["Gender"]=="Male")["Total"])
t_PBm_2008
Out[1618]:
In [1619]:
t_PBfm_2008 = np.sum(df_st_PB_2008.where(df_st_PB_2008["Gender"]=="Female")["Total"])
t_PBfm_2008
Out[1619]:
In [1620]:
t_RJm_2008 = np.sum(df_st_RJ_2008.where(df_st_RJ_2008["Gender"]=="Male")["Total"])
t_RJm_2008
Out[1620]:
In [1621]:
t_RJfm_2008 = np.sum(df_st_RJ_2008.where(df_st_RJ_2008["Gender"]=="Female")["Total"])
t_RJfm_2008
Out[1621]:
In [1622]:
t_SMm_2008 = np.sum(df_st_SM_2008.where(df_st_SM_2008["Gender"]=="Male")["Total"])
t_SMm_2008
Out[1622]:
In [1623]:
t_SMfm_2008 = np.sum(df_st_SM_2008.where(df_st_SM_2008["Gender"]=="Female")["Total"])
t_SMfm_2008
Out[1623]:
In [1624]:
t_TNm_2008 = np.sum(df_st_TN_2008.where(df_st_TN_2008["Gender"]=="Male")["Total"])
t_TNm_2008
Out[1624]:
In [1625]:
t_TNfm_2008 = np.sum(df_st_TN_2008.where(df_st_TN_2008["Gender"]=="Female")["Total"])
t_TNfm_2008
Out[1625]:
In [1626]:
t_TPm_2008 = np.sum(df_st_TP_2008.where(df_st_TP_2008["Gender"]=="Male")["Total"])
t_TPm_2008
Out[1626]:
In [1627]:
t_TPfm_2008 = np.sum(df_st_TP_2008.where(df_st_TP_2008["Gender"]=="Female")["Total"])
t_TPfm_2008
Out[1627]:
In [1628]:
t_UPm_2008 = np.sum(df_st_UP_2008.where(df_st_UP_2008["Gender"]=="Male")["Total"])
t_UPm_2008
Out[1628]:
In [1629]:
t_UPfm_2008 = np.sum(df_st_UP_2008.where(df_st_UP_2008["Gender"]=="Female")["Total"])
t_UPfm_2008
Out[1629]:
In [1630]:
t_UKm_2008 = np.sum(df_st_UK_2008.where(df_st_UK_2008["Gender"]=="Male")["Total"])
t_UKm_2008
Out[1630]:
In [1631]:
t_UKfm_2008 = np.sum(df_st_UK_2008.where(df_st_UK_2008["Gender"]=="Female")["Total"])
t_UKfm_2008
Out[1631]:
In [1632]:
t_WBm_2008 = np.sum(df_st_WB_2008.where(df_st_WB_2008["Gender"]=="Male")["Total"])
t_WBm_2008
Out[1632]:
In [1633]:
t_WBfm_2008 = np.sum(df_st_WB_2008.where(df_st_WB_2008["Gender"]=="Female")["Total"])
t_WBfm_2008
Out[1633]:
In [1634]:
l_2008=list(df_2008.loc[:,"Type"].unique())
l_2008.sort()
print(l_2008,end=" ")
In [1635]:
cause_l_2008=[]
for i in l_2008:
rough= np.sum(df_2008.where(df_2008["Type"]==i)["Total"])
cause_l_2008.append(rough)
print(cause_l_2008,end=" ")
In [1636]:
x1_2008 = set(df_2008.loc[:,"State"])
x1_2008
len(x1_2008)
Out[1636]:
In [1637]:
cause_df_2008 = pd.DataFrame(cause_l_2008,l_2008)
cause_df_2008.reset_index()
Out[1637]:
In [1638]:
cause_df_2008.plot.bar(stacked=True,figsize=(50,20))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("Causes",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths due to Different Causes in 2008",fontsize=55)
plt.legend(["Death Count"], prop={"size":30})
Out[1638]:
In [1639]:
s_2008=list(df_2008.loc[:,"State"].unique())
s_2008.sort()
print(s_2008,end=" ")
In [1640]:
state_s_2008 = [s8_19,s8_20,s8_21,s8_22,s8_23,s8_24,s8_25,s8_26,s8_27,s8_28,s8_29,s8_30,s8_31,s8_32,s8_33,s8_34,
s8_35,s8_36,s8_37,s8_38,s8_39,s8_40,s8_41,s8_42,s8_43,s8_44,s8_45,s8_46,s8_47,s8_48,s8_49,s8_50,
s8_51,s8_52,s8_53]
print(state_s_2008,end=" ")
In [1641]:
state_m_2008=[t_ANm_2008, t_APm_2008, t_ARPm_2008, t_ASm_2008, t_BHm_2008, t_CHm_2008, t_CTm_2008, t_DNm_2008,
t_DDm_2008, t_DLm_2008, t_GOAm_2008, t_GJm_2008, t_HRm_2008, t_HPm_2008,t_JKm_2008, t_JHm_2008,
t_KNm_2008, t_KERm_2008, t_LDm_2008,t_MPm_2008,t_MHm_2008,t_MNm_2008,t_MGm_2008,t_MZm_2008,
t_NGm_2008, t_ODm_2008, t_PDm_2008,t_PBm_2008,t_RJm_2008,t_SMm_2008,t_TNm_2008,t_TPm_2008,
t_UPm_2008, t_UKm_2008, t_WBm_2008]
print(state_m_2008,end=" ")
In [1642]:
state_fm_2008=[t_ANfm_2008, t_APfm_2008, t_ARPfm_2008, t_ASfm_2008, t_BHfm_2008, t_CHfm_2008, t_CTfm_2008, t_DNfm_2008,
t_DDfm_2008, t_DLfm_2008, t_GOAfm_2008, t_GJfm_2008, t_HRfm_2008, t_HPfm_2008,t_JKfm_2008, t_JHfm_2008,
t_KNfm_2008, t_KERfm_2008, t_LDfm_2008,t_MPfm_2008,t_MHfm_2008,t_MNfm_2008,t_MGfm_2008,t_MZfm_2008,
t_NGfm_2008, t_ODfm_2008, t_PDfm_2008,t_PBfm_2008,t_RJfm_2008,t_SMfm_2008,t_TNfm_2008,t_TPfm_2008,
t_UPfm_2008, t_UKfm_2008, t_WBfm_2008]
print(state_fm_2008,end=" ")
In [1643]:
df_state_2008 = {
"State":['A & N ISLANDS', 'ANDHRA PRADESH', 'ARUNACHAL PRADESH', 'ASSAM', 'BIHAR', 'CHANDIGARH', 'CHHATTISGARH',
'D & N HAVELI', 'DAMAN & DIU', 'DELHI (UT)', 'GOA', 'GUJARAT', 'HARYANA', 'HIMACHAL PRADESH', 'JAMMU & KASHMIR',
'JHARKHAND', 'KARNATAKA', 'KERALA', 'LAKSHADWEEP', 'MADHYA PRADESH', 'MAHARASHTRA', 'MANIPUR', 'MEGHALAYA',
'MIZORAM', 'NAGALAND', 'ODISHA', 'PUDUCHERRY', 'PUNJAB', 'RAJASTHAN', 'SIKKIM', 'TAMIL NADU', 'TRIPURA',
'UTTAR PRADESH', 'UTTARAKHAND', 'WEST BENGAL'],
"Total":[s8_19,s8_20,s8_21,s8_22,s8_23,s8_24,s8_25,s8_26,s8_27,s8_28,s8_29,s8_30,s8_31,s8_32,s8_33,s8_34,
s8_35,s8_36,s8_37,s8_38,s8_39,s8_40,s8_41,s8_42,s8_43,s8_44,s8_45,s8_46,s8_47,s8_48,s8_49,s8_50,
s8_51,s8_52,s8_53],
"Male":[t_ANm_2008, t_APm_2008, t_ARPm_2008, t_ASm_2008, t_BHm_2008, t_CHm_2008, t_CTm_2008, t_DNm_2008,
t_DDm_2008, t_DLm_2008, t_GOAm_2008, t_GJm_2008, t_HRm_2008, t_HPm_2008,t_JKm_2008, t_JHm_2008,
t_KNm_2008, t_KERm_2008, t_LDm_2008,t_MPm_2008,t_MHm_2008,t_MNm_2008,t_MGm_2008,t_MZm_2008,
t_NGm_2008, t_ODm_2008, t_PDm_2008,t_PBm_2008,t_RJm_2008,t_SMm_2008,t_TNm_2008,t_TPm_2008,
t_UPm_2008, t_UKm_2008, t_WBm_2008],
"Female":[t_ANfm_2008, t_APfm_2008, t_ARPfm_2008, t_ASfm_2008, t_BHfm_2008, t_CHfm_2008, t_CTfm_2008, t_DNfm_2008,
t_DDfm_2008, t_DLfm_2008, t_GOAfm_2008, t_GJfm_2008, t_HRfm_2008, t_HPfm_2008,t_JKfm_2008, t_JHfm_2008,
t_KNfm_2008, t_KERfm_2008, t_LDfm_2008,t_MPfm_2008,t_MHfm_2008,t_MNfm_2008,t_MGfm_2008,t_MZfm_2008,
t_NGfm_2008, t_ODfm_2008, t_PDfm_2008,t_PBfm_2008,t_RJfm_2008,t_SMfm_2008,t_TNfm_2008,t_TPfm_2008,
t_UPfm_2008, t_UKfm_2008, t_WBfm_2008]
}
df_All_st_2008 = pd.DataFrame(df_state_2008)
df_All_st_2008
Out[1643]:
In [1644]:
pivot_2008 = pd.pivot_table(df_All_st_2008, values=None, index='State', columns=None,
fill_value=None, margins=False, dropna=True, margins_name='All', observed=False)
pivot_2008
Out[1644]:
In [1645]:
pivot_2008.plot(kind='bar',figsize=(60,30))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("States",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths of Males And Females In Different State",fontsize=55)
plt.legend(["Female","Male","Total"], prop={"size":30})
plt.show
Out[1645]:
In [1646]:
cause_l_percent_2008=[]
for i in cause_l_2008:
p=i/np.sum(cause_l_2008)
avg_2008_cause=p*100
cause_l_percent_2008.append(avg_2008_cause)
print(cause_l_percent_2008,end=" ")
In [1647]:
np.max(cause_l_percent_2008) #Married
Out[1647]:
In [1648]:
np.min(cause_l_percent_2008) #Ideological Causes/Hero Worshipping
Out[1648]:
In [1649]:
state_s_2008_percent=[]
tot=np.sum(state_s_2008)
for i in state_s_2008:
xy=i/tot
avg_state_2008=xy*100
state_s_2008_percent.append(avg_state_2008)
print(state_s_2008_percent,end=" ")
In [1650]:
np.max(state_s_2008_percent) #Maharashtra
Out[1650]:
In [1651]:
np.min(state_s_2008_percent) #Lakshdweep
Out[1651]:
In [1652]:
l_age_grp_2008=[s8_4,s8_5,s8_6,s8_7,s8_8,s8_54]
l_age_grp_2008
Out[1652]:
In [1653]:
l_age_grp_2008_percent=[]
for i in l_age_grp_2008:
yz=i/s8_1
avg_2008_age_grp=yz*100
l_age_grp_2008_percent.append(avg_2008_age_grp)
l_age_grp_2008_percent
Out[1653]:
In [1654]:
np.max(l_age_grp_2008_percent) #0-100+
Out[1654]:
In [1655]:
np.min(l_age_grp_2008_percent) #0-14
Out[1655]:
In [1656]:
total_male_percent_2008= (s8_2/s8_1)*100
total_male_percent_2008
Out[1656]:
In [1657]:
total_female_percent_2008= (s8_3/s8_1)*100
total_female_percent_2008
Out[1657]:
In [ ]:
2009¶
In [1658]:
df_2009 = suicide_df.loc[(suicide_df["Year"]==2009)]
df_2009
Out[1658]:
In [1659]:
t2009_dcount = df_2009.loc[:,"Total"]
s9_1 = np.sum(t2009_dcount)
s9_1
Out[1659]:
In [1660]:
male_2009 = df_2009.loc[(df_2009["Gender"]=="Male")]
male_2009
Out[1660]:
In [1661]:
m2009_count = male_2009.loc[:,"Total"]
s9_2 = np.sum(m2009_count)
s9_2
Out[1661]:
In [1662]:
female_2009 = df_2009.loc[(df_2009["Gender"]=="Female")]
female_2009
Out[1662]:
In [1663]:
fm2009_count = female_2009.loc[:,"Total"]
s9_3 = np.sum(fm2009_count)
s9_3
Out[1663]:
In [1664]:
df_age_d014_2009 = df_2009.loc[(df_2009["Age_group"]=="0-14")]
df_age_d014_2009
Out[1664]:
In [1665]:
t_014_2009 = df_age_d014_2009.loc[:,"Total"]
s9_4 = np.sum(t_014_2009)
s9_4
Out[1665]:
In [1666]:
df_age_d1529_2009 = df_2009.loc[(df_2009["Age_group"]=="15-29")]
df_age_d1529_2009
Out[1666]:
In [1667]:
t_1529_2009 = df_age_d1529_2009.loc[:,"Total"]
s9_5 = np.sum(t_1529_2009)
s9_5
Out[1667]:
In [1668]:
df_age_d3044_2009 = df_2009.loc[(df_2009["Age_group"]=="30-44")]
df_age_d3044_2009
Out[1668]:
In [1669]:
t_3044_2009 = df_age_d3044_2009.loc[:,"Total"]
s9_6 = np.sum(t_3044_2009)
s9_6
Out[1669]:
In [1670]:
df_age_d4559_2009 = df_2009.loc[(df_2009["Age_group"]=="45-59")]
df_age_d4559_2009
Out[1670]:
In [1671]:
t_4559_2009 = df_age_d4559_2009.loc[:,"Total"]
s9_7 = np.sum(t_4559_2009)
s9_7
Out[1671]:
In [1672]:
df_age_d60_2009 = df_2009.loc[(df_2009["Age_group"]=="60+")]
df_age_d60_2009
Out[1672]:
In [1673]:
t_60_2009 = df_age_d60_2009.loc[:,"Total"]
s9_8 = np.sum(t_60_2009)
s9_8
Out[1673]:
In [1674]:
df_age_d0100_2009 = df_2009.loc[(df_2009["Age_group"]=="0-100+")]
df_age_d0100_2009
Out[1674]:
In [1675]:
t_0100_2009 = df_age_d0100_2009.loc[:,"Total"]
s9_54 = np.sum(t_0100_2009)
s9_54
Out[1675]:
In [1676]:
df_014m_2009 = df_age_d014_2009.loc[(df_age_d014_2009["Gender"]=="Male")]
df_014m_2009
Out[1676]:
In [1677]:
t_014m_2009 = df_014m_2009.loc[:,"Total"]
s9_9 = np.sum(t_014m_2009)
s9_9
Out[1677]:
In [1678]:
df_014fm_2009 = df_age_d014_2009.loc[(df_age_d014_2009["Gender"]=="Female")]
df_014fm_2009
Out[1678]:
In [1679]:
t_014fm_2009 = df_014fm_2009.loc[:,"Total"]
s9_10 = np.sum(t_014fm_2009)
s9_10
Out[1679]:
In [1680]:
df_1529m_2009 = df_age_d1529_2009.loc[(df_age_d1529_2009["Gender"]=="Male")]
df_1529m_2009
Out[1680]:
In [1681]:
t_1529m_2009 = df_1529m_2009.loc[:,"Total"]
s9_11 = np.sum(t_1529m_2009)
s9_11
Out[1681]:
In [1682]:
df_1529fm_2009 = df_age_d1529_2009.loc[(df_age_d1529_2009["Gender"]=="Female")]
df_1529fm_2009
Out[1682]:
In [1683]:
t_1529fm_2009 = df_1529fm_2009.loc[:,"Total"]
s9_12 = np.sum(t_1529fm_2009)
s9_12
Out[1683]:
In [1684]:
df_3044m_2009 = df_age_d3044_2009.loc[(df_age_d3044_2009["Gender"]=="Male")]
df_3044m_2009
Out[1684]:
In [1685]:
t_3044m_2009 = df_3044m_2009.loc[:,"Total"]
s9_13 = np.sum(t_3044m_2009)
s9_13
Out[1685]:
In [1686]:
df_3044fm_2009 = df_age_d3044_2009.loc[(df_age_d3044_2009["Gender"]=="Female")]
df_3044fm_2009
Out[1686]:
In [1687]:
t_3044fm_2009 = df_3044fm_2009.loc[:,"Total"]
s9_14 = np.sum(t_3044fm_2009)
s9_14
Out[1687]:
In [1688]:
df_4559m_2009 = df_age_d4559_2009.loc[(df_age_d4559_2009["Gender"]=="Male")]
df_4559m_2009
Out[1688]:
In [1689]:
t_4559m_2009 = df_4559m_2009.loc[:,"Total"]
s9_15 = np.sum(t_4559m_2009)
s9_15
Out[1689]:
In [1690]:
df_4559fm_2009 = df_age_d4559_2009.loc[(df_age_d4559_2009["Gender"]=="Female")]
df_4559fm_2009
Out[1690]:
In [1691]:
t_4559fm_2009 = df_4559fm_2009.loc[:,"Total"]
s9_16 = np.sum(t_4559fm_2009)
s9_16
Out[1691]:
In [1692]:
df_60m_2009 = df_age_d60_2009.loc[(df_age_d60_2009["Gender"]=="Male")]
df_60m_2009
Out[1692]:
In [1693]:
t_60m_2009 = df_60m_2009.loc[:,"Total"]
s9_17 = np.sum(t_60m_2009)
s9_17
Out[1693]:
In [1694]:
df_60fm_2009 = df_age_d60_2009.loc[(df_age_d60_2009["Gender"]=="Female")]
df_60fm_2009
Out[1694]:
In [1695]:
t_60fm_2009 = df_60fm_2009.loc[:,"Total"]
s9_18 = np.sum(t_60fm_2009)
s9_18
Out[1695]:
In [1696]:
df_0100fm_2009 = df_age_d0100_2009.loc[(df_age_d0100_2009["Gender"]=="Female")]
df_0100fm_2009
Out[1696]:
In [1697]:
t_0100fm_2009 = df_0100fm_2009.loc[:,"Total"]
s9_55 = np.sum(t_0100fm_2009)
s9_55
Out[1697]:
In [1698]:
df_0100m_2009 = df_age_d0100_2009.loc[(df_age_d0100_2009["Gender"]=="Male")]
df_0100m_2009
Out[1698]:
In [1699]:
t_0100m_2009 = df_0100m_2009.loc[:,"Total"]
s9_56 = np.sum(t_0100m_2009)
s9_56
Out[1699]:
In [1700]:
df_st_AN_2009 = df_2009.loc[(df_2009["State"]=="A & N ISLANDS")]
df_st_AN_2009
Out[1700]:
In [1701]:
t_AN_2009 = df_st_AN_2009.loc[:,"Total"]
s9_19 = np.sum(t_AN_2009)
s9_19
Out[1701]:
In [1702]:
df_st_AP_2009 = df_2009.loc[(df_2009["State"]=="ANDHRA PRADESH")]
df_st_AP_2009
Out[1702]:
In [1703]:
t_AP_2009 = df_st_AP_2009.loc[:,"Total"]
s9_20 = np.sum(t_AP_2009)
s9_20
Out[1703]:
In [1704]:
df_st_ARP_2009 = df_2009.loc[(df_2009["State"]=="ARUNACHAL PRADESH")]
df_st_ARP_2009
Out[1704]:
In [1705]:
t_ARP_2009 = df_st_ARP_2009.loc[:,"Total"]
s9_21 = np.sum(t_ARP_2009)
s9_21
Out[1705]:
In [1706]:
df_st_AS_2009 = df_2009.loc[(df_2009["State"]=="ASSAM")]
df_st_AS_2009
Out[1706]:
In [1707]:
t_AS_2009 = df_st_AS_2009.loc[:,"Total"]
s9_22 = np.sum(t_AS_2009)
s9_22
Out[1707]:
In [1708]:
df_st_BH_2009 = df_2009.loc[(df_2009["State"]=="BIHAR")]
df_st_BH_2009
Out[1708]:
In [1709]:
t_BH_2009 = df_st_BH_2009.loc[:,"Total"]
s9_23 = np.sum(t_BH_2009)
s9_23
Out[1709]:
In [1710]:
df_st_CH_2009 = df_2009.loc[(df_2009["State"]=="CHANDIGARH")]
df_st_CH_2009
Out[1710]:
In [1711]:
t_CH_2009 = df_st_CH_2009.loc[:,"Total"]
s9_24 = np.sum(t_CH_2009)
s9_24
Out[1711]:
In [1712]:
df_st_CT_2009 = df_2009.loc[(df_2009["State"]=="CHHATTISGARH")]
df_st_CT_2009
Out[1712]:
In [1713]:
t_CT_2009 = df_st_CT_2009.loc[:,"Total"]
s9_25 = np.sum(t_CT_2009)
s9_25
Out[1713]:
In [1714]:
df_st_DN_2009 = df_2009.loc[(df_2009["State"]=="D & N HAVELI")]
df_st_DN_2009
Out[1714]:
In [1715]:
t_DN_2009 = df_st_DN_2009.loc[:,"Total"]
s9_26 = np.sum(t_DN_2009)
s9_26
Out[1715]:
In [1716]:
df_st_DD_2009 = df_2009.loc[(df_2009["State"]=="DAMAN & DIU")]
df_st_DD_2009
Out[1716]:
In [1717]:
t_DD_2009 = df_st_DD_2009.loc[:,"Total"]
s9_27 = np.sum(t_DD_2009)
s9_27
Out[1717]:
In [1718]:
df_st_DL_2009 = df_2009.loc[(df_2009["State"]=="DELHI (UT)")]
df_st_DL_2009
Out[1718]:
In [1719]:
t_DL_2009 = df_st_DL_2009.loc[:,"Total"]
s9_28 = np.sum(t_DL_2009)
s9_28
Out[1719]:
In [1720]:
df_st_GOA_2009 = df_2009.loc[(df_2009["State"]=="GOA")]
df_st_GOA_2009
Out[1720]:
In [1721]:
t_GOA_2009 = df_st_GOA_2009.loc[:,"Total"]
s9_29 = np.sum(t_GOA_2009)
s9_29
Out[1721]:
In [1722]:
df_st_GJ_2009 = df_2009.loc[(df_2009["State"]=="GUJARAT")]
df_st_GJ_2009
Out[1722]:
In [1723]:
t_GJ_2009 = df_st_GJ_2009.loc[:,"Total"]
s9_30 = np.sum(t_GJ_2009)
s9_30
Out[1723]:
In [1724]:
df_st_HR_2009 = df_2009.loc[(df_2009["State"]=="HARYANA")]
df_st_HR_2009
Out[1724]:
In [1725]:
t_HR_2009 = df_st_HR_2009.loc[:,"Total"]
s9_31 = np.sum(t_HR_2009)
s9_31
Out[1725]:
In [1726]:
df_st_HP_2009 = df_2009.loc[(df_2009["State"]=="HIMACHAL PRADESH")]
df_st_HP_2009
Out[1726]:
In [1727]:
t_HP_2009 = df_st_HP_2009.loc[:,"Total"]
s9_32 = np.sum(t_HP_2009)
s9_32
Out[1727]:
In [1728]:
df_st_JK_2009 = df_2009.loc[(df_2009["State"]=="JAMMU & KASHMIR")]
df_st_JK_2009
Out[1728]:
In [1729]:
t_JK_2009 = df_st_JK_2009.loc[:,"Total"]
s9_33 = np.sum(t_JK_2009)
s9_33
Out[1729]:
In [1730]:
df_st_JH_2009 = df_2009.loc[(df_2009["State"]=="JHARKHAND")]
df_st_JH_2009
Out[1730]:
In [1731]:
t_JH_2009 = df_st_JH_2009.loc[:,"Total"]
s9_34 = np.sum(t_JH_2009)
s9_34
Out[1731]:
In [1732]:
df_st_KN_2009 = df_2009.loc[(df_2009["State"]=="KARNATAKA")]
df_st_KN_2009
Out[1732]:
In [1733]:
t_KN_2009 = df_st_KN_2009.loc[:,"Total"]
s9_35 = np.sum(t_KN_2009)
s9_35
Out[1733]:
In [1734]:
df_st_KER_2009 = df_2009.loc[(df_2009["State"]=="KERALA")]
df_st_KER_2009
Out[1734]:
In [1735]:
t_KER_2009 = df_st_KER_2009.loc[:,"Total"]
s9_36 = np.sum(t_KER_2009)
s9_36
Out[1735]:
In [1736]:
df_st_LD_2009 = df_2009.loc[(df_2009["State"]=="LAKSHADWEEP")]
df_st_LD_2009
Out[1736]:
In [1737]:
t_LD_2009 = df_st_LD_2009.loc[:,"Total"]
s9_37 = np.sum(t_LD_2009)
s9_37
Out[1737]:
In [1738]:
df_st_MP_2009 = df_2009.loc[(df_2009["State"]=="MADHYA PRADESH")]
df_st_MP_2009
Out[1738]:
In [1739]:
t_MP_2009 = df_st_MP_2009.loc[:,"Total"]
s9_38 = np.sum(t_MP_2009)
s9_38
Out[1739]:
In [1740]:
df_st_MH_2009 = df_2009.loc[(df_2009["State"]=="MAHARASHTRA")]
df_st_MH_2009
Out[1740]:
In [1741]:
t_MH_2009 = df_st_MH_2009.loc[:,"Total"]
s9_39 = np.sum(t_MH_2009)
s9_39
Out[1741]:
In [1742]:
df_st_MN_2009 = df_2009.loc[(df_2009["State"]=="MANIPUR")]
df_st_MN_2009
Out[1742]:
In [1743]:
t_MN_2009 = df_st_MN_2009.loc[:,"Total"]
s9_40 = np.sum(t_MN_2009)
s9_40
Out[1743]:
In [1744]:
df_st_MG_2009 = df_2009.loc[(df_2009["State"]=="MEGHALAYA")]
df_st_MG_2009
Out[1744]:
In [1745]:
t_MG_2009 = df_st_MG_2009.loc[:,"Total"]
s9_41 = np.sum(t_MG_2009)
s9_41
Out[1745]:
In [1746]:
df_st_MZ_2009 = df_2009.loc[(df_2009["State"]=="MIZORAM")]
df_st_MZ_2009
Out[1746]:
In [1747]:
t_MN_2009 = df_st_MN_2009.loc[:,"Total"]
s9_42 = np.sum(t_MN_2009)
s9_42
Out[1747]:
In [1748]:
df_st_NG_2009 = df_2009.loc[(df_2009["State"]=="NAGALAND")]
df_st_NG_2009
Out[1748]:
In [1749]:
t_NG_2009 = df_st_NG_2009.loc[:,"Total"]
s9_43 = np.sum(t_NG_2009)
s9_43
Out[1749]:
In [1750]:
df_st_OD_2009 = df_2009.loc[(df_2009["State"]=="ODISHA")]
df_st_OD_2009
Out[1750]:
In [1751]:
t_OD_2009 = df_st_OD_2009.loc[:,"Total"]
s9_44 = np.sum(t_OD_2009)
s9_44
Out[1751]:
In [1752]:
df_st_PD_2009 = df_2009.loc[(df_2009["State"]=="PUDUCHERRY")]
df_st_PD_2009
Out[1752]:
In [1753]:
t_PD_2009 = df_st_PD_2009.loc[:,"Total"]
s9_45 = np.sum(t_PD_2009)
s9_45
Out[1753]:
In [1754]:
df_st_PB_2009 = df_2009.loc[(df_2009["State"]=="PUNJAB")]
df_st_PB_2009
Out[1754]:
In [1755]:
t_PB_2009 = df_st_PB_2009.loc[:,"Total"]
s9_46 = np.sum(t_PB_2009)
s9_46
Out[1755]:
In [1756]:
df_st_RJ_2009 = df_2009.loc[(df_2009["State"]=="RAJASTHAN")]
df_st_RJ_2009
Out[1756]:
In [1757]:
t_RJ_2009 = df_st_RJ_2009.loc[:,"Total"]
s9_47 = np.sum(t_RJ_2009)
s9_47
Out[1757]:
In [1758]:
df_st_SM_2009 = df_2009.loc[(df_2009["State"]=="SIKKIM")]
df_st_SM_2009
Out[1758]:
In [1759]:
t_SM_2009 = df_st_SM_2009.loc[:,"Total"]
s9_48 = np.sum(t_SM_2009)
s9_48
Out[1759]:
In [1760]:
df_st_TN_2009 = df_2009.loc[(df_2009["State"]=="TAMIL NADU")]
df_st_TN_2009
Out[1760]:
In [1761]:
t_TN_2009 = df_st_TN_2009.loc[:,"Total"]
s9_49 = np.sum(t_TN_2009)
s9_49
Out[1761]:
In [1762]:
df_st_TP_2009 = df_2009.loc[(df_2009["State"]=="TRIPURA")]
df_st_TP_2009
Out[1762]:
In [1763]:
t_TP_2009 = df_st_TP_2009.loc[:,"Total"]
s9_50 = np.sum(t_TP_2009)
s9_50
Out[1763]:
In [1764]:
df_st_UP_2009 = df_2009.loc[(df_2009["State"]=="UTTAR PRADESH")]
df_st_UP_2009
Out[1764]:
In [1765]:
t_UP_2009 = df_st_UP_2009.loc[:,"Total"]
s9_51 = np.sum(t_UP_2009)
s9_51
Out[1765]:
In [1766]:
df_st_UK_2009 = df_2009.loc[(df_2009["State"]=="UTTARAKHAND")]
df_st_UK_2009
Out[1766]:
In [1767]:
t_UK_2009 = df_st_UK_2009.loc[:,"Total"]
s9_52 = np.sum(t_UK_2009)
s9_52
Out[1767]:
In [1768]:
df_st_WB_2009 = df_2009.loc[(df_2009["State"]=="WEST BENGAL")]
df_st_WB_2009
Out[1768]:
In [1769]:
t_WB_2009 = df_st_WB_2009.loc[:,"Total"]
s9_53 = np.sum(t_WB_2009)
s9_53
Out[1769]:
In [1770]:
t_ANm_2009 = np.sum(df_st_AN_2009.where(df_st_AN_2009["Gender"]=="Male")["Total"])
t_ANm_2009
Out[1770]:
In [1771]:
t_ANfm_2009 = np.sum(df_st_AN_2009.where(df_st_AN_2009["Gender"]=="Female")["Total"])
t_ANfm_2009
Out[1771]:
In [1772]:
t_APm_2009 = np.sum(df_st_AP_2009.where(df_st_AP_2009["Gender"]=="Male")["Total"])
t_APm_2009
Out[1772]:
In [1773]:
t_APfm_2009 = np.sum(df_st_AP_2009.where(df_st_AP_2009["Gender"]=="Female")["Total"])
t_APfm_2009
Out[1773]:
In [1774]:
t_ARPm_2009 = np.sum(df_st_ARP_2009.where(df_st_ARP_2009["Gender"]=="Male")["Total"])
t_ARPm_2009
Out[1774]:
In [1775]:
t_ARPfm_2009 = np.sum(df_st_ARP_2009.where(df_st_ARP_2009["Gender"]=="Female")["Total"])
t_ARPfm_2009
Out[1775]:
In [1776]:
t_ASm_2009 = np.sum(df_st_AS_2009.where(df_st_AS_2009["Gender"]=="Male")["Total"])
t_ASm_2009
Out[1776]:
In [1777]:
t_ASfm_2009 = np.sum(df_st_AS_2009.where(df_st_AS_2009["Gender"]=="Female")["Total"])
t_ASfm_2009
Out[1777]:
In [1778]:
t_BHm_2009 = np.sum(df_st_BH_2009.where(df_st_BH_2009["Gender"]=="Male")["Total"])
t_BHm_2009
Out[1778]:
In [1779]:
t_BHfm_2009 = np.sum(df_st_BH_2009.where(df_st_BH_2009["Gender"]=="Female")["Total"])
t_BHfm_2009
Out[1779]:
In [1780]:
t_CHm_2009 = np.sum(df_st_CH_2009.where(df_st_CH_2009["Gender"]=="Male")["Total"])
t_CHm_2009
Out[1780]:
In [1781]:
t_CHfm_2009 = np.sum(df_st_CH_2009.where(df_st_CH_2009["Gender"]=="Female")["Total"])
t_CHfm_2009
Out[1781]:
In [1782]:
t_CTm_2009 = np.sum(df_st_CT_2009.where(df_st_CT_2009["Gender"]=="Male")["Total"])
t_CTm_2009
Out[1782]:
In [1783]:
t_CTfm_2009 = np.sum(df_st_CT_2009.where(df_st_CT_2009["Gender"]=="Female")["Total"])
t_CTfm_2009
Out[1783]:
In [1784]:
t_DNm_2009 = np.sum(df_st_DN_2009.where(df_st_DN_2009["Gender"]=="Male")["Total"])
t_DNm_2009
Out[1784]:
In [1785]:
t_DNfm_2009 = np.sum(df_st_DN_2009.where(df_st_DN_2009["Gender"]=="Female")["Total"])
t_DNfm_2009
Out[1785]:
In [1786]:
t_DDm_2009 = np.sum(df_st_DD_2009.where(df_st_DD_2009["Gender"]=="Male")["Total"])
t_DDm_2009
Out[1786]:
In [1787]:
t_DDfm_2009 = np.sum(df_st_DD_2009.where(df_st_DD_2009["Gender"]=="Female")["Total"])
t_DDfm_2009
Out[1787]:
In [1788]:
t_DLm_2009 = np.sum(df_st_DL_2009.where(df_st_DL_2009["Gender"]=="Male")["Total"])
t_DLm_2009
Out[1788]:
In [1789]:
t_DLfm_2009 = np.sum(df_st_DL_2009.where(df_st_DL_2009["Gender"]=="Female")["Total"])
t_DLfm_2009
Out[1789]:
In [1790]:
t_GOAm_2009 = np.sum(df_st_GOA_2009.where(df_st_GOA_2009["Gender"]=="Male")["Total"])
t_GOAm_2009
Out[1790]:
In [1791]:
t_GOAfm_2009 = np.sum(df_st_GOA_2009.where(df_st_GOA_2009["Gender"]=="Female")["Total"])
t_GOAfm_2009
Out[1791]:
In [1792]:
t_GJm_2009 = np.sum(df_st_GJ_2009.where(df_st_GJ_2009["Gender"]=="Male")["Total"])
t_GJm_2009
Out[1792]:
In [1793]:
t_GJfm_2009 = np.sum(df_st_GJ_2009.where(df_st_GJ_2009["Gender"]=="Female")["Total"])
t_GJfm_2009
Out[1793]:
In [1794]:
t_HRm_2009 = np.sum(df_st_HR_2009.where(df_st_HR_2009["Gender"]=="Male")["Total"])
t_HRm_2009
Out[1794]:
In [1795]:
t_HRfm_2009 = np.sum(df_st_HR_2009.where(df_st_HR_2009["Gender"]=="Female")["Total"])
t_HRfm_2009
Out[1795]:
In [1796]:
t_HPm_2009 = np.sum(df_st_HP_2009.where(df_st_HP_2009["Gender"]=="Male")["Total"])
t_HPm_2009
Out[1796]:
In [1797]:
t_HPfm_2009 = np.sum(df_st_HP_2009.where(df_st_HP_2009["Gender"]=="Female")["Total"])
t_HPfm_2009
Out[1797]:
In [1798]:
t_JKm_2009 = np.sum(df_st_JK_2009.where(df_st_JK_2009["Gender"]=="Male")["Total"])
t_JKm_2009
Out[1798]:
In [1799]:
t_JKfm_2009 = np.sum(df_st_JK_2009.where(df_st_JK_2009["Gender"]=="Female")["Total"])
t_JKfm_2009
Out[1799]:
In [1800]:
t_JHm_2009 = np.sum(df_st_JH_2009.where(df_st_JH_2009["Gender"]=="Male")["Total"])
t_JHm_2009
Out[1800]:
In [1801]:
t_JHfm_2009 = np.sum(df_st_JH_2009.where(df_st_JH_2009["Gender"]=="Female")["Total"])
t_JHfm_2009
Out[1801]:
In [1802]:
t_KNm_2009 = np.sum(df_st_KN_2009.where(df_st_KN_2009["Gender"]=="Male")["Total"])
t_KNm_2009
Out[1802]:
In [1803]:
t_KNfm_2009 = np.sum(df_st_KN_2009.where(df_st_KN_2009["Gender"]=="Female")["Total"])
t_KNfm_2009
Out[1803]:
In [1804]:
t_KERm_2009 = np.sum(df_st_KER_2009.where(df_st_KER_2009["Gender"]=="Male")["Total"])
t_KERm_2009
Out[1804]:
In [1805]:
t_KERfm_2009 = np.sum(df_st_KER_2009.where(df_st_KER_2009["Gender"]=="Female")["Total"])
t_KERfm_2009
Out[1805]:
In [1806]:
t_LDm_2009 = np.sum(df_st_LD_2009.where(df_st_LD_2009["Gender"]=="Male")["Total"])
t_LDm_2009
Out[1806]:
In [1807]:
t_LDfm_2009 = np.sum(df_st_LD_2009.where(df_st_LD_2009["Gender"]=="Female")["Total"])
t_LDfm_2009
Out[1807]:
In [1808]:
t_MPm_2009 = np.sum(df_st_MP_2009.where(df_st_MP_2009["Gender"]=="Male")["Total"])
t_MPm_2009
Out[1808]:
In [1809]:
t_MPfm_2009 = np.sum(df_st_MP_2009.where(df_st_MP_2009["Gender"]=="Female")["Total"])
t_MPfm_2009
Out[1809]:
In [1810]:
t_MHm_2009 = np.sum(df_st_MH_2009.where(df_st_MH_2009["Gender"]=="Male")["Total"])
t_MHm_2009
Out[1810]:
In [1811]:
t_MHfm_2009 = np.sum(df_st_MH_2009.where(df_st_MH_2009["Gender"]=="Female")["Total"])
t_MHfm_2009
Out[1811]:
In [1812]:
t_MNm_2009 = np.sum(df_st_MN_2009.where(df_st_MN_2009["Gender"]=="Male")["Total"])
t_MNm_2009
Out[1812]:
In [1813]:
t_MNfm_2009 = np.sum(df_st_MN_2009.where(df_st_MN_2009["Gender"]=="Female")["Total"])
t_MNfm_2009
Out[1813]:
In [1814]:
t_MGm_2009 = np.sum(df_st_MG_2009.where(df_st_MG_2009["Gender"]=="Male")["Total"])
t_MGm_2009
Out[1814]:
In [1815]:
t_MGfm_2009 = np.sum(df_st_MG_2009.where(df_st_MG_2009["Gender"]=="Female")["Total"])
t_MGfm_2009
Out[1815]:
In [1816]:
t_MZm_2009 = np.sum(df_st_MZ_2009.where(df_st_MZ_2009["Gender"]=="Male")["Total"])
t_MZm_2009
Out[1816]:
In [1817]:
t_MZfm_2009 = np.sum(df_st_MZ_2009.where(df_st_MZ_2009["Gender"]=="Female")["Total"])
t_MZfm_2009
Out[1817]:
In [1818]:
t_NGm_2009 = np.sum(df_st_NG_2009.where(df_st_NG_2009["Gender"]=="Male")["Total"])
t_NGm_2009
Out[1818]:
In [1819]:
t_NGfm_2009 = np.sum(df_st_NG_2009.where(df_st_NG_2009["Gender"]=="Female")["Total"])
t_MGfm_2009
Out[1819]:
In [1820]:
t_ODm_2009 = np.sum(df_st_OD_2009.where(df_st_OD_2009["Gender"]=="Male")["Total"])
t_ODm_2009
Out[1820]:
In [1821]:
t_ODfm_2009 = np.sum(df_st_OD_2009.where(df_st_OD_2009["Gender"]=="Female")["Total"])
t_ODfm_2009
Out[1821]:
In [1822]:
t_PDm_2009 = np.sum(df_st_PD_2009.where(df_st_PD_2009["Gender"]=="Male")["Total"])
t_PDm_2009
Out[1822]:
In [1823]:
t_PDfm_2009 = np.sum(df_st_PD_2009.where(df_st_PD_2009["Gender"]=="Female")["Total"])
t_PDfm_2009
Out[1823]:
In [1824]:
t_PBm_2009 = np.sum(df_st_PB_2009.where(df_st_PB_2009["Gender"]=="Male")["Total"])
t_PBm_2009
Out[1824]:
In [1825]:
t_PBfm_2009 = np.sum(df_st_PB_2009.where(df_st_PB_2009["Gender"]=="Female")["Total"])
t_PBfm_2009
Out[1825]:
In [1826]:
t_RJm_2009 = np.sum(df_st_RJ_2009.where(df_st_RJ_2009["Gender"]=="Male")["Total"])
t_RJm_2009
Out[1826]:
In [1827]:
t_RJfm_2009 = np.sum(df_st_RJ_2009.where(df_st_RJ_2009["Gender"]=="Female")["Total"])
t_RJfm_2009
Out[1827]:
In [1828]:
t_SMm_2009 = np.sum(df_st_SM_2009.where(df_st_SM_2009["Gender"]=="Male")["Total"])
t_SMm_2009
Out[1828]:
In [1829]:
t_SMfm_2009 = np.sum(df_st_SM_2009.where(df_st_SM_2009["Gender"]=="Female")["Total"])
t_SMfm_2009
Out[1829]:
In [1830]:
t_TNm_2009 = np.sum(df_st_TN_2009.where(df_st_TN_2009["Gender"]=="Male")["Total"])
t_TNm_2009
Out[1830]:
In [1831]:
t_TNfm_2009 = np.sum(df_st_TN_2009.where(df_st_TN_2009["Gender"]=="Female")["Total"])
t_TNfm_2009
Out[1831]:
In [1832]:
t_TPm_2009 = np.sum(df_st_TP_2009.where(df_st_TP_2009["Gender"]=="Male")["Total"])
t_TPm_2009
Out[1832]:
In [1833]:
t_TPfm_2009 = np.sum(df_st_TP_2009.where(df_st_TP_2009["Gender"]=="Female")["Total"])
t_TPfm_2009
Out[1833]:
In [1834]:
t_UPm_2009 = np.sum(df_st_UP_2009.where(df_st_UP_2009["Gender"]=="Male")["Total"])
t_UPm_2009
Out[1834]:
In [1835]:
t_UPfm_2009 = np.sum(df_st_UP_2009.where(df_st_UP_2009["Gender"]=="Female")["Total"])
t_UPfm_2009
Out[1835]:
In [1836]:
t_UKm_2009 = np.sum(df_st_UK_2009.where(df_st_UK_2009["Gender"]=="Male")["Total"])
t_UKm_2009
Out[1836]:
In [1837]:
t_UKfm_2009 = np.sum(df_st_UK_2009.where(df_st_UK_2009["Gender"]=="Female")["Total"])
t_UKfm_2009
Out[1837]:
In [1838]:
t_WBm_2009 = np.sum(df_st_WB_2009.where(df_st_WB_2009["Gender"]=="Male")["Total"])
t_WBm_2009
Out[1838]:
In [1839]:
t_WBfm_2009 = np.sum(df_st_WB_2009.where(df_st_WB_2009["Gender"]=="Female")["Total"])
t_WBfm_2009
Out[1839]:
In [1840]:
l_2009=list(df_2009.loc[:,"Type"].unique())
l_2009.sort()
print(l_2009,end=" ")
In [1841]:
cause_l_2009=[]
for i in l_2009:
rough= np.sum(df_2009.where(df_2009["Type"]==i)["Total"])
cause_l_2009.append(rough)
print(cause_l_2009,end=" ")
In [1842]:
x1_2009 = set(df_2009.loc[:,"State"])
x1_2009
len(x1_2009)
Out[1842]:
In [1843]:
cause_df_2009 = pd.DataFrame(cause_l_2009,l_2009)
cause_df_2009.reset_index()
Out[1843]:
In [1844]:
cause_df_2009.plot.bar(stacked=True,figsize=(50,20))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("Causes",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths due to Different Causes in 2009",fontsize=55)
plt.legend(["Death Count"], prop={"size":30})
Out[1844]:
In [1845]:
s_2009=list(df_2009.loc[:,"State"].unique())
s_2009.sort()
print(s_2009,end=" ")
In [1846]:
state_s_2009 = [s9_19,s9_20,s9_21,s9_22,s9_23,s9_24,s9_25,s9_26,s9_27,s9_28,s9_29,s9_30,s9_31,s9_32,s9_33,s9_34,
s9_35,s9_36,s9_37,s9_38,s9_39,s9_40,s9_41,s9_42,s9_43,s9_44,s9_45,s9_46,s9_47,s9_48,s9_49,s9_50,
s9_51,s9_52,s9_53]
print(state_s_2009,end=" ")
In [1847]:
state_m_2009=[t_ANm_2009, t_APm_2009, t_ARPm_2009, t_ASm_2009, t_BHm_2009, t_CHm_2009, t_CTm_2009, t_DNm_2009,
t_DDm_2009, t_DLm_2009, t_GOAm_2009, t_GJm_2009, t_HRm_2009, t_HPm_2009,t_JKm_2009, t_JHm_2009,
t_KNm_2009, t_KERm_2009, t_LDm_2009,t_MPm_2009,t_MHm_2009,t_MNm_2009,t_MGm_2009,t_MZm_2009,
t_NGm_2009, t_ODm_2009, t_PDm_2009,t_PBm_2009,t_RJm_2009,t_SMm_2009,t_TNm_2009,t_TPm_2009,
t_UPm_2009, t_UKm_2009, t_WBm_2009]
print(state_m_2009,end=" ")
In [1848]:
state_fm_2009=[t_ANfm_2009, t_APfm_2009, t_ARPfm_2009, t_ASfm_2009, t_BHfm_2009, t_CHfm_2009, t_CTfm_2009, t_DNfm_2009,
t_DDfm_2009, t_DLfm_2009, t_GOAfm_2009, t_GJfm_2009, t_HRfm_2009, t_HPfm_2009,t_JKfm_2009, t_JHfm_2009,
t_KNfm_2009, t_KERfm_2009, t_LDfm_2009,t_MPfm_2009,t_MHfm_2009,t_MNfm_2009,t_MGfm_2009,t_MZfm_2009,
t_NGfm_2009, t_ODfm_2009, t_PDfm_2009,t_PBfm_2009,t_RJfm_2009,t_SMfm_2009,t_TNfm_2009,t_TPfm_2009,
t_UPfm_2009, t_UKfm_2009, t_WBfm_2009]
print(state_fm_2009,end=" ")
In [1849]:
df_state_2009 = {
"State":['A & N ISLANDS', 'ANDHRA PRADESH', 'ARUNACHAL PRADESH', 'ASSAM', 'BIHAR', 'CHANDIGARH', 'CHHATTISGARH',
'D & N HAVELI', 'DAMAN & DIU', 'DELHI (UT)', 'GOA', 'GUJARAT', 'HARYANA', 'HIMACHAL PRADESH', 'JAMMU & KASHMIR',
'JHARKHAND', 'KARNATAKA', 'KERALA', 'LAKSHADWEEP', 'MADHYA PRADESH', 'MAHARASHTRA', 'MANIPUR', 'MEGHALAYA',
'MIZORAM', 'NAGALAND', 'ODISHA', 'PUDUCHERRY', 'PUNJAB', 'RAJASTHAN', 'SIKKIM', 'TAMIL NADU', 'TRIPURA',
'UTTAR PRADESH', 'UTTARAKHAND', 'WEST BENGAL'],
"Total":[s9_19,s9_20,s9_21,s9_22,s9_23,s9_24,s9_25,s9_26,s9_27,s9_28,s9_29,s9_30,s9_31,s9_32,s9_33,s9_34,
s9_35,s9_36,s9_37,s9_38,s9_39,s9_40,s9_41,s9_42,s9_43,s9_44,s9_45,s9_46,s9_47,s9_48,s9_49,s9_50,
s9_51,s9_52,s9_53],
"Male":[t_ANm_2009, t_APm_2009, t_ARPm_2009, t_ASm_2009, t_BHm_2009, t_CHm_2009, t_CTm_2009, t_DNm_2009,
t_DDm_2009, t_DLm_2009, t_GOAm_2009, t_GJm_2009, t_HRm_2009, t_HPm_2009,t_JKm_2009, t_JHm_2009,
t_KNm_2009, t_KERm_2009, t_LDm_2009,t_MPm_2009,t_MHm_2009,t_MNm_2009,t_MGm_2009,t_MZm_2009,
t_NGm_2009, t_ODm_2009, t_PDm_2009,t_PBm_2009,t_RJm_2009,t_SMm_2009,t_TNm_2009,t_TPm_2009,
t_UPm_2009, t_UKm_2009, t_WBm_2009],
"Female":[t_ANfm_2009, t_APfm_2009, t_ARPfm_2009, t_ASfm_2009, t_BHfm_2009, t_CHfm_2009, t_CTfm_2009, t_DNfm_2009,
t_DDfm_2009, t_DLfm_2009, t_GOAfm_2009, t_GJfm_2009, t_HRfm_2009, t_HPfm_2009,t_JKfm_2009, t_JHfm_2009,
t_KNfm_2009, t_KERfm_2009, t_LDfm_2009,t_MPfm_2009,t_MHfm_2009,t_MNfm_2009,t_MGfm_2009,t_MZfm_2009,
t_NGfm_2009, t_ODfm_2009, t_PDfm_2009,t_PBfm_2009,t_RJfm_2009,t_SMfm_2009,t_TNfm_2009,t_TPfm_2009,
t_UPfm_2009, t_UKfm_2009, t_WBfm_2009]
}
df_All_st_2009 = pd.DataFrame(df_state_2009)
df_All_st_2009
Out[1849]:
In [2482]:
pivot_2009 = pd.pivot_table(df_All_st_2009, values=None, index='State', columns=None,
fill_value=None, margins=False, dropna=True, margins_name='All', observed=False)
pivot_2009
Out[2482]:
In [2483]:
pivot_2009.plot(kind='bar',figsize=(60,30))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("States",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths of Males And Females In Different State",fontsize=55)
plt.legend(["Female","Male","Total"], prop={"size":30})
plt.show
Out[2483]:
In [2484]:
cause_l_percent_2009=[]
for i in cause_l_2009:
p=i/np.sum(cause_l_2009)
avg_2009_cause=p*100
cause_l_percent_2009.append(avg_2009_cause)
print(cause_l_percent_2009,end=" ")
In [2485]:
np.max(cause_l_percent_2009) #Married
Out[2485]:
In [2486]:
np.min(cause_l_percent_2009) #Ideological Causes/Hero Worshipping
Out[2486]:
In [2487]:
state_s_2009_percent=[]
tot=np.sum(state_s_2009)
for i in state_s_2009:
xy=i/tot
avg_state_2009=xy*100
state_s_2009_percent.append(avg_state_2009)
print(state_s_2009_percent,end=" ")
In [2488]:
np.max(state_s_2009_percent) #Maharashtra
Out[2488]:
In [2489]:
np.min(state_s_2009_percent) #Lakshdweep
Out[2489]:
In [2490]:
l_age_grp_2009=[s9_4,s9_5,s9_6,s9_7,s9_8,s9_54]
l_age_grp_2009
Out[2490]:
In [2491]:
l_age_grp_2009_percent=[]
for i in l_age_grp_2009:
yz=i/s9_1
avg_2009_age_grp=yz*100
l_age_grp_2009_percent.append(avg_2009_age_grp)
l_age_grp_2009_percent
Out[2491]:
In [2492]:
np.max(l_age_grp_2009_percent) #0-100+
Out[2492]:
In [2493]:
np.min(l_age_grp_2009_percent) #0-14
Out[2493]:
In [2494]:
total_male_percent_2009= (s9_2/s9_1)*100
total_male_percent_2009
Out[2494]:
In [2495]:
total_female_percent_2009= (s9_3/s9_1)*100
total_female_percent_2009
Out[2495]:
In [ ]:
2010¶
In [2496]:
df_2010 = suicide_df.loc[(suicide_df["Year"]==2010)]
df_2010
Out[2496]:
In [2497]:
t2010_dcount = df_2010.loc[:,"Total"]
s10_1 = np.sum(t2010_dcount)
s10_1
Out[2497]:
In [2498]:
male_2010 = df_2010.loc[(df_2010["Gender"]=="Male")]
male_2010
Out[2498]:
In [2499]:
m2010_count = male_2010.loc[:,"Total"]
s10_2 = np.sum(m2010_count)
s10_2
Out[2499]:
In [2500]:
female_2010 = df_2010.loc[(df_2010["Gender"]=="Female")]
female_2010
Out[2500]:
In [2501]:
fm2010_count = female_2010.loc[:,"Total"]
s10_3 = np.sum(fm2010_count)
s10_3
Out[2501]:
In [2502]:
df_age_d014_2010 = df_2010.loc[(df_2010["Age_group"]=="0-14")]
df_age_d014_2010
Out[2502]:
In [2503]:
t_014_2010 = df_age_d014_2010.loc[:,"Total"]
s10_4 = np.sum(t_014_2010)
s10_4
Out[2503]:
In [2504]:
df_age_d1529_2010 = df_2010.loc[(df_2010["Age_group"]=="15-29")]
df_age_d1529_2010
Out[2504]:
In [2505]:
t_1529_2010 = df_age_d1529_2010.loc[:,"Total"]
s10_5 = np.sum(t_1529_2010)
s10_5
Out[2505]:
In [2506]:
df_age_d3044_2010 = df_2010.loc[(df_2010["Age_group"]=="30-44")]
df_age_d3044_2010
Out[2506]:
In [2507]:
t_3044_2010 = df_age_d3044_2010.loc[:,"Total"]
s10_6 = np.sum(t_3044_2010)
s10_6
Out[2507]:
In [2508]:
df_age_d4559_2010 = df_2010.loc[(df_2010["Age_group"]=="45-59")]
df_age_d4559_2010
Out[2508]:
In [2509]:
t_4559_2010 = df_age_d4559_2010.loc[:,"Total"]
s10_7 = np.sum(t_4559_2010)
s10_7
Out[2509]:
In [2510]:
df_age_d60_2010 = df_2010.loc[(df_2010["Age_group"]=="60+")]
df_age_d60_2010
Out[2510]:
In [2511]:
t_60_2010 = df_age_d60_2010.loc[:,"Total"]
s10_8 = np.sum(t_60_2010)
s10_8
Out[2511]:
In [2512]:
df_age_d0100_2010 = df_2010.loc[(df_2010["Age_group"]=="0-100+")]
df_age_d0100_2010
Out[2512]:
In [2513]:
t_0100_2010 = df_age_d0100_2010.loc[:,"Total"]
s10_54 = np.sum(t_0100_2010)
s10_54
Out[2513]:
In [2514]:
df_014m_2010 = df_age_d014_2010.loc[(df_age_d014_2010["Gender"]=="Male")]
df_014m_2010
Out[2514]:
In [2515]:
t_014m_2010 = df_014m_2010.loc[:,"Total"]
s10_9 = np.sum(t_014m_2010)
s10_9
Out[2515]:
In [2516]:
df_014fm_2010 = df_age_d014_2010.loc[(df_age_d014_2010["Gender"]=="Female")]
df_014fm_2010
Out[2516]:
In [2517]:
t_014fm_2010 = df_014fm_2010.loc[:,"Total"]
s10_10 = np.sum(t_014fm_2010)
s10_10
Out[2517]:
In [2518]:
df_1529m_2010 = df_age_d1529_2010.loc[(df_age_d1529_2010["Gender"]=="Male")]
df_1529m_2010
Out[2518]:
In [2519]:
t_1529m_2010 = df_1529m_2010.loc[:,"Total"]
s10_11 = np.sum(t_1529m_2010)
s10_11
Out[2519]:
In [2520]:
df_1529fm_2010 = df_age_d1529_2010.loc[(df_age_d1529_2010["Gender"]=="Female")]
df_1529fm_2010
Out[2520]:
In [2521]:
t_1529fm_2010 = df_1529fm_2010.loc[:,"Total"]
s10_12 = np.sum(t_1529fm_2010)
s10_12
Out[2521]:
In [2522]:
df_3044m_2010 = df_age_d3044_2010.loc[(df_age_d3044_2010["Gender"]=="Male")]
df_3044m_2010
Out[2522]:
In [2523]:
t_3044m_2010 = df_3044m_2010.loc[:,"Total"]
s10_13 = np.sum(t_3044m_2010)
s10_13
Out[2523]:
In [2524]:
df_3044fm_2010 = df_age_d3044_2010.loc[(df_age_d3044_2010["Gender"]=="Female")]
df_3044fm_2010
Out[2524]:
In [2525]:
t_3044fm_2010 = df_3044fm_2010.loc[:,"Total"]
s10_14 = np.sum(t_3044fm_2010)
s10_14
Out[2525]:
In [2526]:
df_4559m_2010 = df_age_d4559_2010.loc[(df_age_d4559_2010["Gender"]=="Male")]
df_4559m_2010
Out[2526]:
In [2527]:
t_4559m_2010 = df_4559m_2010.loc[:,"Total"]
s10_15 = np.sum(t_4559m_2010)
s10_15
Out[2527]:
In [2528]:
df_4559fm_2010 = df_age_d4559_2010.loc[(df_age_d4559_2010["Gender"]=="Female")]
df_4559fm_2010
Out[2528]:
In [2529]:
t_4559fm_2010 = df_4559fm_2010.loc[:,"Total"]
s10_16 = np.sum(t_4559fm_2010)
s10_16
Out[2529]:
In [2530]:
df_60m_2010 = df_age_d60_2010.loc[(df_age_d60_2010["Gender"]=="Male")]
df_60m_2010
Out[2530]:
In [2531]:
t_60m_2010 = df_60m_2010.loc[:,"Total"]
s10_17 = np.sum(t_60m_2010)
s10_17
Out[2531]:
In [2532]:
df_60fm_2010 = df_age_d60_2010.loc[(df_age_d60_2010["Gender"]=="Female")]
df_60fm_2010
Out[2532]:
In [2533]:
t_60fm_2010 = df_60fm_2010.loc[:,"Total"]
s10_18 = np.sum(t_60fm_2010)
s10_18
Out[2533]:
In [2534]:
df_0100fm_2010 = df_age_d0100_2010.loc[(df_age_d0100_2010["Gender"]=="Female")]
df_0100fm_2010
Out[2534]:
In [2535]:
t_0100fm_2010 = df_0100fm_2010.loc[:,"Total"]
s10_55 = np.sum(t_0100fm_2010)
s10_55
Out[2535]:
In [2536]:
df_0100m_2010 = df_age_d0100_2010.loc[(df_age_d0100_2010["Gender"]=="Male")]
df_0100m_2010
Out[2536]:
In [2537]:
t_0100m_2010 = df_0100m_2010.loc[:,"Total"]
s10_56 = np.sum(t_0100m_2010)
s10_56
Out[2537]:
In [2538]:
df_st_AN_2010 = df_2010.loc[(df_2010["State"]=="A & N ISLANDS")]
df_st_AN_2010
Out[2538]:
In [2539]:
t_AN_2010 = df_st_AN_2010.loc[:,"Total"]
s10_19 = np.sum(t_AN_2010)
s10_19
Out[2539]:
In [2540]:
df_st_AP_2010 = df_2010.loc[(df_2010["State"]=="ANDHRA PRADESH")]
df_st_AP_2010
Out[2540]:
In [2541]:
t_AP_2010 = df_st_AP_2010.loc[:,"Total"]
s10_20 = np.sum(t_AP_2010)
s10_20
Out[2541]:
In [2542]:
df_st_ARP_2010 = df_2010.loc[(df_2010["State"]=="ARUNACHAL PRADESH")]
df_st_ARP_2010
Out[2542]:
In [2543]:
t_ARP_2010 = df_st_ARP_2010.loc[:,"Total"]
s10_21 = np.sum(t_ARP_2010)
s10_21
Out[2543]:
In [2544]:
df_st_AS_2010 = df_2010.loc[(df_2010["State"]=="ASSAM")]
df_st_AS_2010
Out[2544]:
In [2545]:
t_AS_2010 = df_st_AS_2010.loc[:,"Total"]
s10_22 = np.sum(t_AS_2010)
s10_22
Out[2545]:
In [2546]:
df_st_BH_2010 = df_2010.loc[(df_2010["State"]=="BIHAR")]
df_st_BH_2010
Out[2546]:
In [2547]:
t_BH_2010 = df_st_BH_2010.loc[:,"Total"]
s10_23 = np.sum(t_BH_2010)
s10_23
Out[2547]:
In [2548]:
df_st_CH_2010 = df_2010.loc[(df_2010["State"]=="CHANDIGARH")]
df_st_CH_2010
Out[2548]:
In [2549]:
t_CH_2010 = df_st_CH_2010.loc[:,"Total"]
s10_24 = np.sum(t_CH_2010)
s10_24
Out[2549]:
In [2550]:
df_st_CT_2010 = df_2010.loc[(df_2010["State"]=="CHHATTISGARH")]
df_st_CT_2010
Out[2550]:
In [2551]:
t_CT_2010 = df_st_CT_2010.loc[:,"Total"]
s10_25 = np.sum(t_CT_2010)
s10_25
Out[2551]:
In [2552]:
df_st_DN_2010 = df_2010.loc[(df_2010["State"]=="D & N HAVELI")]
df_st_DN_2010
Out[2552]:
In [2553]:
t_DN_2010 = df_st_DN_2010.loc[:,"Total"]
s10_26 = np.sum(t_DN_2010)
s10_26
Out[2553]:
In [2554]:
df_st_DD_2010 = df_2010.loc[(df_2010["State"]=="DAMAN & DIU")]
df_st_DD_2010
Out[2554]:
In [2555]:
t_DD_2010 = df_st_DD_2010.loc[:,"Total"]
s10_27 = np.sum(t_DD_2010)
s10_27
Out[2555]:
In [2556]:
df_st_DL_2010 = df_2010.loc[(df_2010["State"]=="DELHI (UT)")]
df_st_DL_2010
Out[2556]:
In [2557]:
t_DL_2010 = df_st_DL_2010.loc[:,"Total"]
s10_28 = np.sum(t_DL_2010)
s10_28
Out[2557]:
In [2558]:
df_st_GOA_2010 = df_2010.loc[(df_2010["State"]=="GOA")]
df_st_GOA_2010
Out[2558]:
In [2559]:
t_GOA_2010 = df_st_GOA_2010.loc[:,"Total"]
s10_29 = np.sum(t_GOA_2010)
s10_29
Out[2559]:
In [2560]:
df_st_GJ_2010 = df_2010.loc[(df_2010["State"]=="GUJARAT")]
df_st_GJ_2010
Out[2560]:
In [2561]:
t_GJ_2010 = df_st_GJ_2010.loc[:,"Total"]
s10_30 = np.sum(t_GJ_2010)
s10_30
Out[2561]:
In [2562]:
df_st_HR_2010 = df_2010.loc[(df_2010["State"]=="HARYANA")]
df_st_HR_2010
Out[2562]:
In [2563]:
t_HR_2010 = df_st_HR_2010.loc[:,"Total"]
s10_31 = np.sum(t_HR_2010)
s10_31
Out[2563]:
In [2564]:
df_st_HP_2010 = df_2010.loc[(df_2010["State"]=="HIMACHAL PRADESH")]
df_st_HP_2010
Out[2564]:
In [2565]:
t_HP_2010 = df_st_HP_2010.loc[:,"Total"]
s10_32 = np.sum(t_HP_2010)
s10_32
Out[2565]:
In [2566]:
df_st_JK_2010 = df_2010.loc[(df_2010["State"]=="JAMMU & KASHMIR")]
df_st_JK_2010
Out[2566]:
In [2567]:
t_JK_2010 = df_st_JK_2010.loc[:,"Total"]
s10_33 = np.sum(t_JK_2010)
s10_33
Out[2567]:
In [2568]:
df_st_JH_2010 = df_2010.loc[(df_2010["State"]=="JHARKHAND")]
df_st_JH_2010
Out[2568]:
In [2569]:
t_JH_2010 = df_st_JH_2010.loc[:,"Total"]
s10_34 = np.sum(t_JH_2010)
s10_34
Out[2569]:
In [2570]:
df_st_KN_2010 = df_2010.loc[(df_2010["State"]=="KARNATAKA")]
df_st_KN_2010
Out[2570]:
In [2571]:
t_KN_2010 = df_st_KN_2010.loc[:,"Total"]
s10_35 = np.sum(t_KN_2010)
s10_35
Out[2571]:
In [2572]:
df_st_KER_2010 = df_2010.loc[(df_2010["State"]=="KERALA")]
df_st_KER_2010
Out[2572]:
In [2573]:
t_KER_2010 = df_st_KER_2010.loc[:,"Total"]
s10_36 = np.sum(t_KER_2010)
s10_36
Out[2573]:
In [2574]:
df_st_LD_2010 = df_2010.loc[(df_2010["State"]=="LAKSHADWEEP")]
df_st_LD_2010
Out[2574]:
In [2575]:
t_LD_2010 = df_st_LD_2010.loc[:,"Total"]
s10_37 = np.sum(t_LD_2010)
s10_37
Out[2575]:
In [2576]:
df_st_MP_2010 = df_2010.loc[(df_2010["State"]=="MADHYA PRADESH")]
df_st_MP_2010
Out[2576]:
In [2577]:
t_MP_2010 = df_st_MP_2010.loc[:,"Total"]
s10_38 = np.sum(t_MP_2010)
s10_38
Out[2577]:
In [2578]:
df_st_MH_2010 = df_2010.loc[(df_2010["State"]=="MAHARASHTRA")]
df_st_MH_2010
Out[2578]:
In [2579]:
t_MH_2010 = df_st_MH_2010.loc[:,"Total"]
s10_39 = np.sum(t_MH_2010)
s10_39
Out[2579]:
In [2580]:
df_st_MN_2010 = df_2010.loc[(df_2010["State"]=="MANIPUR")]
df_st_MN_2010
Out[2580]:
In [2581]:
t_MN_2010 = df_st_MN_2010.loc[:,"Total"]
s10_40 = np.sum(t_MN_2010)
s10_40
Out[2581]:
In [2582]:
df_st_MG_2010 = df_2010.loc[(df_2010["State"]=="MEGHALAYA")]
df_st_MG_2010
Out[2582]:
In [2583]:
t_MG_2010 = df_st_MG_2010.loc[:,"Total"]
s10_41 = np.sum(t_MG_2010)
s10_41
Out[2583]:
In [2584]:
df_st_MZ_2010 = df_2010.loc[(df_2010["State"]=="MIZORAM")]
df_st_MZ_2010
Out[2584]:
In [2585]:
t_MN_2010 = df_st_MN_2010.loc[:,"Total"]
s10_42 = np.sum(t_MN_2010)
s10_42
Out[2585]:
In [2586]:
df_st_NG_2010 = df_2010.loc[(df_2010["State"]=="NAGALAND")]
df_st_NG_2010
Out[2586]:
In [2587]:
t_NG_2010 = df_st_NG_2010.loc[:,"Total"]
s10_43 = np.sum(t_NG_2010)
s10_43
Out[2587]:
In [2588]:
df_st_OD_2010 = df_2010.loc[(df_2010["State"]=="ODISHA")]
df_st_OD_2010
Out[2588]:
In [2589]:
t_OD_2010 = df_st_OD_2010.loc[:,"Total"]
s10_44 = np.sum(t_OD_2010)
s10_44
Out[2589]:
In [2590]:
df_st_PD_2010 = df_2010.loc[(df_2010["State"]=="PUDUCHERRY")]
df_st_PD_2010
Out[2590]:
In [2591]:
t_PD_2010 = df_st_PD_2010.loc[:,"Total"]
s10_45 = np.sum(t_PD_2010)
s10_45
Out[2591]:
In [2592]:
df_st_PB_2010 = df_2010.loc[(df_2010["State"]=="PUNJAB")]
df_st_PB_2010
Out[2592]:
In [2593]:
t_PB_2010 = df_st_PB_2010.loc[:,"Total"]
s10_46 = np.sum(t_PB_2010)
s10_46
Out[2593]:
In [2594]:
df_st_RJ_2010 = df_2010.loc[(df_2010["State"]=="RAJASTHAN")]
df_st_RJ_2010
Out[2594]:
In [2595]:
t_RJ_2010 = df_st_RJ_2010.loc[:,"Total"]
s10_47 = np.sum(t_RJ_2010)
s10_47
Out[2595]:
In [2596]:
df_st_SM_2010 = df_2010.loc[(df_2010["State"]=="SIKKIM")]
df_st_SM_2010
Out[2596]:
In [2597]:
t_SM_2010 = df_st_SM_2010.loc[:,"Total"]
s10_48 = np.sum(t_SM_2010)
s10_48
Out[2597]:
In [2598]:
df_st_TN_2010 = df_2010.loc[(df_2010["State"]=="TAMIL NADU")]
df_st_TN_2010
Out[2598]:
In [2599]:
t_TN_2010 = df_st_TN_2010.loc[:,"Total"]
s10_49 = np.sum(t_TN_2010)
s10_49
Out[2599]:
In [2600]:
df_st_TP_2010 = df_2010.loc[(df_2010["State"]=="TRIPURA")]
df_st_TP_2010
Out[2600]:
In [2601]:
t_TP_2010 = df_st_TP_2010.loc[:,"Total"]
s10_50 = np.sum(t_TP_2010)
s10_50
Out[2601]:
In [2602]:
df_st_UP_2010 = df_2010.loc[(df_2010["State"]=="UTTAR PRADESH")]
df_st_UP_2010
Out[2602]:
In [2603]:
t_UP_2010 = df_st_UP_2010.loc[:,"Total"]
s10_51 = np.sum(t_UP_2010)
s10_51
Out[2603]:
In [2604]:
df_st_UK_2010 = df_2010.loc[(df_2010["State"]=="UTTARAKHAND")]
df_st_UK_2010
Out[2604]:
In [2605]:
t_UK_2010 = df_st_UK_2010.loc[:,"Total"]
s10_52 = np.sum(t_UK_2010)
s10_52
Out[2605]:
In [2606]:
df_st_WB_2010 = df_2010.loc[(df_2010["State"]=="WEST BENGAL")]
df_st_WB_2010
Out[2606]:
In [2607]:
t_WB_2010 = df_st_WB_2010.loc[:,"Total"]
s10_53 = np.sum(t_WB_2010)
s10_53
Out[2607]:
In [2608]:
t_ANm_2010 = np.sum(df_st_AN_2010.where(df_st_AN_2010["Gender"]=="Male")["Total"])
t_ANm_2010
Out[2608]:
In [2609]:
t_ANfm_2010 = np.sum(df_st_AN_2010.where(df_st_AN_2010["Gender"]=="Female")["Total"])
t_ANfm_2010
Out[2609]:
In [2610]:
t_APm_2010 = np.sum(df_st_AP_2010.where(df_st_AP_2010["Gender"]=="Male")["Total"])
t_APm_2010
Out[2610]:
In [2611]:
t_APfm_2010 = np.sum(df_st_AP_2010.where(df_st_AP_2010["Gender"]=="Female")["Total"])
t_APfm_2010
Out[2611]:
In [2612]:
t_ARPm_2010 = np.sum(df_st_ARP_2010.where(df_st_ARP_2010["Gender"]=="Male")["Total"])
t_ARPm_2010
Out[2612]:
In [2613]:
t_ARPfm_2010 = np.sum(df_st_ARP_2010.where(df_st_ARP_2010["Gender"]=="Female")["Total"])
t_ARPfm_2010
Out[2613]:
In [2614]:
t_ASm_2010 = np.sum(df_st_AS_2010.where(df_st_AS_2010["Gender"]=="Male")["Total"])
t_ASm_2010
Out[2614]:
In [2615]:
t_ASfm_2010 = np.sum(df_st_AS_2010.where(df_st_AS_2010["Gender"]=="Female")["Total"])
t_ASfm_2010
Out[2615]:
In [2616]:
t_BHm_2010 = np.sum(df_st_BH_2010.where(df_st_BH_2010["Gender"]=="Male")["Total"])
t_BHm_2010
Out[2616]:
In [2617]:
t_BHfm_2010 = np.sum(df_st_BH_2010.where(df_st_BH_2010["Gender"]=="Female")["Total"])
t_BHfm_2010
Out[2617]:
In [2618]:
t_CHm_2010 = np.sum(df_st_CH_2010.where(df_st_CH_2010["Gender"]=="Male")["Total"])
t_CHm_2010
Out[2618]:
In [2619]:
t_CHfm_2010 = np.sum(df_st_CH_2010.where(df_st_CH_2010["Gender"]=="Female")["Total"])
t_CHfm_2010
Out[2619]:
In [2620]:
t_CTm_2010 = np.sum(df_st_CT_2010.where(df_st_CT_2010["Gender"]=="Male")["Total"])
t_CTm_2010
Out[2620]:
In [2621]:
t_CTfm_2010 = np.sum(df_st_CT_2010.where(df_st_CT_2010["Gender"]=="Female")["Total"])
t_CTfm_2010
Out[2621]:
In [2622]:
t_DNm_2010 = np.sum(df_st_DN_2010.where(df_st_DN_2010["Gender"]=="Male")["Total"])
t_DNm_2010
Out[2622]:
In [2623]:
t_DNfm_2010 = np.sum(df_st_DN_2010.where(df_st_DN_2010["Gender"]=="Female")["Total"])
t_DNfm_2010
Out[2623]:
In [2624]:
t_DDm_2010 = np.sum(df_st_DD_2010.where(df_st_DD_2010["Gender"]=="Male")["Total"])
t_DDm_2010
Out[2624]:
In [2625]:
t_DDfm_2010 = np.sum(df_st_DD_2010.where(df_st_DD_2010["Gender"]=="Female")["Total"])
t_DDfm_2010
Out[2625]:
In [2626]:
t_DLm_2010 = np.sum(df_st_DL_2010.where(df_st_DL_2010["Gender"]=="Male")["Total"])
t_DLm_2010
Out[2626]:
In [2627]:
t_DLfm_2010 = np.sum(df_st_DL_2010.where(df_st_DL_2010["Gender"]=="Female")["Total"])
t_DLfm_2010
Out[2627]:
In [2628]:
t_GOAm_2010 = np.sum(df_st_GOA_2010.where(df_st_GOA_2010["Gender"]=="Male")["Total"])
t_GOAm_2010
Out[2628]:
In [2629]:
t_GOAfm_2010 = np.sum(df_st_GOA_2010.where(df_st_GOA_2010["Gender"]=="Female")["Total"])
t_GOAfm_2010
Out[2629]:
In [2630]:
t_GJm_2010 = np.sum(df_st_GJ_2010.where(df_st_GJ_2010["Gender"]=="Male")["Total"])
t_GJm_2010
Out[2630]:
In [2631]:
t_GJfm_2010 = np.sum(df_st_GJ_2010.where(df_st_GJ_2010["Gender"]=="Female")["Total"])
t_GJfm_2010
Out[2631]:
In [2632]:
t_HRm_2010 = np.sum(df_st_HR_2010.where(df_st_HR_2010["Gender"]=="Male")["Total"])
t_HRm_2010
Out[2632]:
In [2633]:
t_HRfm_2010 = np.sum(df_st_HR_2010.where(df_st_HR_2010["Gender"]=="Female")["Total"])
t_HRfm_2010
Out[2633]:
In [2634]:
t_HPm_2010 = np.sum(df_st_HP_2010.where(df_st_HP_2010["Gender"]=="Male")["Total"])
t_HPm_2010
Out[2634]:
In [2635]:
t_HPfm_2010 = np.sum(df_st_HP_2010.where(df_st_HP_2010["Gender"]=="Female")["Total"])
t_HPfm_2010
Out[2635]:
In [2636]:
t_JKm_2010 = np.sum(df_st_JK_2010.where(df_st_JK_2010["Gender"]=="Male")["Total"])
t_JKm_2010
Out[2636]:
In [2637]:
t_JKfm_2010 = np.sum(df_st_JK_2010.where(df_st_JK_2010["Gender"]=="Female")["Total"])
t_JKfm_2010
Out[2637]:
In [2638]:
t_JHm_2010 = np.sum(df_st_JH_2010.where(df_st_JH_2010["Gender"]=="Male")["Total"])
t_JHm_2010
Out[2638]:
In [2639]:
t_JHfm_2010 = np.sum(df_st_JH_2010.where(df_st_JH_2010["Gender"]=="Female")["Total"])
t_JHfm_2010
Out[2639]:
In [2640]:
t_KNm_2010 = np.sum(df_st_KN_2010.where(df_st_KN_2010["Gender"]=="Male")["Total"])
t_KNm_2010
Out[2640]:
In [2641]:
t_KNfm_2010 = np.sum(df_st_KN_2010.where(df_st_KN_2010["Gender"]=="Female")["Total"])
t_KNfm_2010
Out[2641]:
In [2642]:
t_KERm_2010 = np.sum(df_st_KER_2010.where(df_st_KER_2010["Gender"]=="Male")["Total"])
t_KERm_2010
Out[2642]:
In [2643]:
t_KERfm_2010 = np.sum(df_st_KER_2010.where(df_st_KER_2010["Gender"]=="Female")["Total"])
t_KERfm_2010
Out[2643]:
In [2644]:
t_LDm_2010 = np.sum(df_st_LD_2010.where(df_st_LD_2010["Gender"]=="Male")["Total"])
t_LDm_2010
Out[2644]:
In [2645]:
t_LDfm_2010 = np.sum(df_st_LD_2010.where(df_st_LD_2010["Gender"]=="Female")["Total"])
t_LDfm_2010
Out[2645]:
In [2646]:
t_MPm_2010 = np.sum(df_st_MP_2010.where(df_st_MP_2010["Gender"]=="Male")["Total"])
t_MPm_2010
Out[2646]:
In [2647]:
t_MPfm_2010 = np.sum(df_st_MP_2010.where(df_st_MP_2010["Gender"]=="Female")["Total"])
t_MPfm_2010
Out[2647]:
In [2648]:
t_MHm_2010 = np.sum(df_st_MH_2010.where(df_st_MH_2010["Gender"]=="Male")["Total"])
t_MHm_2010
Out[2648]:
In [2649]:
t_MHfm_2010 = np.sum(df_st_MH_2010.where(df_st_MH_2010["Gender"]=="Female")["Total"])
t_MHfm_2010
Out[2649]:
In [2650]:
t_MNm_2010 = np.sum(df_st_MN_2010.where(df_st_MN_2010["Gender"]=="Male")["Total"])
t_MNm_2010
Out[2650]:
In [2651]:
t_MNfm_2010 = np.sum(df_st_MN_2010.where(df_st_MN_2010["Gender"]=="Female")["Total"])
t_MNfm_2010
Out[2651]:
In [2652]:
t_MGm_2010 = np.sum(df_st_MG_2010.where(df_st_MG_2010["Gender"]=="Male")["Total"])
t_MGm_2010
Out[2652]:
In [2653]:
t_MGfm_2010 = np.sum(df_st_MG_2010.where(df_st_MG_2010["Gender"]=="Female")["Total"])
t_MGfm_2010
Out[2653]:
In [2654]:
t_MZm_2010 = np.sum(df_st_MZ_2010.where(df_st_MZ_2010["Gender"]=="Male")["Total"])
t_MZm_2010
Out[2654]:
In [2655]:
t_MZfm_2010 = np.sum(df_st_MZ_2010.where(df_st_MZ_2010["Gender"]=="Female")["Total"])
t_MZfm_2010
Out[2655]:
In [2656]:
t_NGm_2010 = np.sum(df_st_NG_2010.where(df_st_NG_2010["Gender"]=="Male")["Total"])
t_NGm_2010
Out[2656]:
In [2657]:
t_NGfm_2010 = np.sum(df_st_NG_2010.where(df_st_NG_2010["Gender"]=="Female")["Total"])
t_MGfm_2010
Out[2657]:
In [2658]:
t_ODm_2010 = np.sum(df_st_OD_2010.where(df_st_OD_2010["Gender"]=="Male")["Total"])
t_ODm_2010
Out[2658]:
In [2659]:
t_ODfm_2010 = np.sum(df_st_OD_2010.where(df_st_OD_2010["Gender"]=="Female")["Total"])
t_ODfm_2010
Out[2659]:
In [2660]:
t_PDm_2010 = np.sum(df_st_PD_2010.where(df_st_PD_2010["Gender"]=="Male")["Total"])
t_PDm_2010
Out[2660]:
In [2661]:
t_PDfm_2010 = np.sum(df_st_PD_2010.where(df_st_PD_2010["Gender"]=="Female")["Total"])
t_PDfm_2010
Out[2661]:
In [2662]:
t_PBm_2010 = np.sum(df_st_PB_2010.where(df_st_PB_2010["Gender"]=="Male")["Total"])
t_PBm_2010
Out[2662]:
In [2663]:
t_PBfm_2010 = np.sum(df_st_PB_2010.where(df_st_PB_2010["Gender"]=="Female")["Total"])
t_PBfm_2010
Out[2663]:
In [2664]:
t_RJm_2010 = np.sum(df_st_RJ_2010.where(df_st_RJ_2010["Gender"]=="Male")["Total"])
t_RJm_2010
Out[2664]:
In [2665]:
t_RJfm_2010 = np.sum(df_st_RJ_2010.where(df_st_RJ_2010["Gender"]=="Female")["Total"])
t_RJfm_2010
Out[2665]:
In [3114]:
t_SMm_2010 = np.sum(df_st_SM_2010.where(df_st_SM_2010["Gender"]=="Male")["Total"])
t_SMm_2010
Out[3114]:
In [3115]:
t_SMfm_2010 = np.sum(df_st_SM_2010.where(df_st_SM_2010["Gender"]=="Female")["Total"])
t_SMfm_2010
Out[3115]:
In [3116]:
t_TNm_2010 = np.sum(df_st_TN_2010.where(df_st_TN_2010["Gender"]=="Male")["Total"])
t_TNm_2010
Out[3116]:
In [3117]:
t_TNfm_2010 = np.sum(df_st_TN_2010.where(df_st_TN_2010["Gender"]=="Female")["Total"])
t_TNfm_2010
Out[3117]:
In [3118]:
t_TPm_2010 = np.sum(df_st_TP_2010.where(df_st_TP_2010["Gender"]=="Male")["Total"])
t_TPm_2010
Out[3118]:
In [3119]:
t_TPfm_2010 = np.sum(df_st_TP_2010.where(df_st_TP_2010["Gender"]=="Female")["Total"])
t_TPfm_2010
Out[3119]:
In [3120]:
t_UPm_2010 = np.sum(df_st_UP_2010.where(df_st_UP_2010["Gender"]=="Male")["Total"])
t_UPm_2010
Out[3120]:
In [3121]:
t_UPfm_2010 = np.sum(df_st_UP_2010.where(df_st_UP_2010["Gender"]=="Female")["Total"])
t_UPfm_2010
Out[3121]:
In [3122]:
t_UKm_2010 = np.sum(df_st_UK_2010.where(df_st_UK_2010["Gender"]=="Male")["Total"])
t_UKm_2010
Out[3122]:
In [3123]:
t_UKfm_2010 = np.sum(df_st_UK_2010.where(df_st_UK_2010["Gender"]=="Female")["Total"])
t_UKfm_2010
Out[3123]:
In [3124]:
t_WBm_2010 = np.sum(df_st_WB_2010.where(df_st_WB_2010["Gender"]=="Male")["Total"])
t_WBm_2010
Out[3124]:
In [3125]:
t_WBfm_2010 = np.sum(df_st_WB_2010.where(df_st_WB_2010["Gender"]=="Female")["Total"])
t_WBfm_2010
Out[3125]:
In [3126]:
l_2010=list(df_2010.loc[:,"Type"].unique())
l_2010.sort()
print(l_2010,end=" ")
In [3127]:
cause_l_2010=[]
for i in l_2010:
rough= np.sum(df_2010.where(df_2010["Type"]==i)["Total"])
cause_l_2010.append(rough)
print(cause_l_2010,end=" ")
In [3128]:
x1_2010 = set(df_2010.loc[:,"State"])
x1_2010
len(x1_2010)
Out[3128]:
In [3129]:
cause_df_2010 = pd.DataFrame(cause_l_2010,l_2010)
cause_df_2010.reset_index()
Out[3129]:
In [3130]:
cause_df_2010.plot.bar(stacked=True,figsize=(50,20))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("Causes",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths due to Different Causes in 2010",fontsize=55)
plt.legend(["Death Count"], prop={"size":30})
Out[3130]:
In [3131]:
s_2010=list(df_2010.loc[:,"State"].unique())
s_2010.sort()
print(s_2010,end=" ")
In [3132]:
state_s_2010 = [s10_19,s10_20,s10_21,s10_22,s10_23,s10_24,s10_25,s10_26,s10_27,s10_28,s10_29,s10_30,s10_31,s10_32,s10_33,s10_34,
s10_35,s10_36,s10_37,s10_38,s10_39,s10_40,s10_41,s10_42,s10_43,s10_44,s10_45,s10_46,s10_47,s10_48,s10_49,s10_50,
s10_51,s10_52,s10_53]
print(state_s_2010,end=" ")
In [3133]:
state_m_2010=[t_ANm_2010, t_APm_2010, t_ARPm_2010, t_ASm_2010, t_BHm_2010, t_CHm_2010, t_CTm_2010, t_DNm_2010,
t_DDm_2010, t_DLm_2010, t_GOAm_2010, t_GJm_2010, t_HRm_2010, t_HPm_2010,t_JKm_2010, t_JHm_2010,
t_KNm_2010, t_KERm_2010, t_LDm_2010,t_MPm_2010,t_MHm_2010,t_MNm_2010,t_MGm_2010,t_MZm_2010,
t_NGm_2010, t_ODm_2010, t_PDm_2010,t_PBm_2010,t_RJm_2010,t_SMm_2010,t_TNm_2010,t_TPm_2010,
t_UPm_2010, t_UKm_2010, t_WBm_2010]
print(state_m_2010,end=" ")
In [3134]:
state_fm_2010=[t_ANfm_2010, t_APfm_2010, t_ARPfm_2010, t_ASfm_2010, t_BHfm_2010, t_CHfm_2010, t_CTfm_2010, t_DNfm_2010,
t_DDfm_2010, t_DLfm_2010, t_GOAfm_2010, t_GJfm_2010, t_HRfm_2010, t_HPfm_2010,t_JKfm_2010, t_JHfm_2010,
t_KNfm_2010, t_KERfm_2010, t_LDfm_2010,t_MPfm_2010,t_MHfm_2010,t_MNfm_2010,t_MGfm_2010,t_MZfm_2010,
t_NGfm_2010, t_ODfm_2010, t_PDfm_2010,t_PBfm_2010,t_RJfm_2010,t_SMfm_2010,t_TNfm_2010,t_TPfm_2010,
t_UPfm_2010, t_UKfm_2010, t_WBfm_2010]
print(state_fm_2010,end=" ")
In [3135]:
df_state_2010 = {
"State":['A & N ISLANDS', 'ANDHRA PRADESH', 'ARUNACHAL PRADESH', 'ASSAM', 'BIHAR', 'CHANDIGARH', 'CHHATTISGARH',
'D & N HAVELI', 'DAMAN & DIU', 'DELHI (UT)', 'GOA', 'GUJARAT', 'HARYANA', 'HIMACHAL PRADESH', 'JAMMU & KASHMIR',
'JHARKHAND', 'KARNATAKA', 'KERALA', 'LAKSHADWEEP', 'MADHYA PRADESH', 'MAHARASHTRA', 'MANIPUR', 'MEGHALAYA',
'MIZORAM', 'NAGALAND', 'ODISHA', 'PUDUCHERRY', 'PUNJAB', 'RAJASTHAN', 'SIKKIM', 'TAMIL NADU', 'TRIPURA',
'UTTAR PRADESH', 'UTTARAKHAND', 'WEST BENGAL'],
"Total":[s10_19,s10_20,s10_21,s10_22,s10_23,s10_24,s10_25,s10_26,s10_27,s10_28,s10_29,s10_30,s10_31,s10_32,s10_33,s10_34,
s10_35,s10_36,s10_37,s10_38,s10_39,s10_40,s10_41,s10_42,s10_43,s10_44,s10_45,s10_46,s10_47,s10_48,s10_49,s10_50,
s10_51,s10_52,s10_53],
"Male":[t_ANm_2010, t_APm_2010, t_ARPm_2010, t_ASm_2010, t_BHm_2010, t_CHm_2010, t_CTm_2010, t_DNm_2010,
t_DDm_2010, t_DLm_2010, t_GOAm_2010, t_GJm_2010, t_HRm_2010, t_HPm_2010,t_JKm_2010, t_JHm_2010,
t_KNm_2010, t_KERm_2010, t_LDm_2010,t_MPm_2010,t_MHm_2010,t_MNm_2010,t_MGm_2010,t_MZm_2010,
t_NGm_2010, t_ODm_2010, t_PDm_2010,t_PBm_2010,t_RJm_2010,t_SMm_2010,t_TNm_2010,t_TPm_2010,
t_UPm_2010, t_UKm_2010, t_WBm_2010],
"Female":[t_ANfm_2010, t_APfm_2010, t_ARPfm_2010, t_ASfm_2010, t_BHfm_2010, t_CHfm_2010, t_CTfm_2010, t_DNfm_2010,
t_DDfm_2010, t_DLfm_2010, t_GOAfm_2010, t_GJfm_2010, t_HRfm_2010, t_HPfm_2010,t_JKfm_2010, t_JHfm_2010,
t_KNfm_2010, t_KERfm_2010, t_LDfm_2010,t_MPfm_2010,t_MHfm_2010,t_MNfm_2010,t_MGfm_2010,t_MZfm_2010,
t_NGfm_2010, t_ODfm_2010, t_PDfm_2010,t_PBfm_2010,t_RJfm_2010,t_SMfm_2010,t_TNfm_2010,t_TPfm_2010,
t_UPfm_2010, t_UKfm_2010, t_WBfm_2010]
}
df_All_st_2010 = pd.DataFrame(df_state_2010)
df_All_st_2010
Out[3135]:
In [3136]:
pivot_2010 = pd.pivot_table(df_All_st_2010, values=None, index='State', columns=None,
fill_value=None, margins=False, dropna=True, margins_name='All', observed=False)
pivot_2010
Out[3136]:
In [3137]:
pivot_2010.plot(kind='bar',figsize=(60,30))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("States",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths of Males And Females In Different State",fontsize=55)
plt.legend(["Female","Male","Total"], prop={"size":30})
plt.show
Out[3137]:
In [3138]:
cause_l_percent_2010=[]
for i in cause_l_2010:
p=i/np.sum(cause_l_2010)
avg_2010_cause=p*100
cause_l_percent_2010.append(avg_2010_cause)
print(cause_l_percent_2010,end=" ")
In [3139]:
np.max(cause_l_percent_2010) #Married
Out[3139]:
In [3140]:
np.min(cause_l_percent_2010) #Ideological Causes/Hero Worshipping
Out[3140]:
In [3141]:
state_s_2010_percent=[]
tot=np.sum(state_s_2010)
for i in state_s_2010:
xy=i/tot
avg_state_2010=xy*100
state_s_2010_percent.append(avg_state_2010)
print(state_s_2010_percent,end=" ")
In [3142]:
np.max(state_s_2010_percent) #Maharashtra
Out[3142]:
In [3143]:
np.min(state_s_2010_percent) #Lakshdweep
Out[3143]:
In [3144]:
l_age_grp_2010=[s10_4,s10_5,s10_6,s10_7,s10_8,s10_54]
l_age_grp_2010
Out[3144]:
In [3145]:
l_age_grp_2010_percent=[]
for i in l_age_grp_2010:
yz=i/s10_1
avg_2010_age_grp=yz*100
l_age_grp_2010_percent.append(avg_2010_age_grp)
l_age_grp_2010_percent
Out[3145]:
In [3146]:
np.max(l_age_grp_2010_percent) #0-100+
Out[3146]:
In [3147]:
np.min(l_age_grp_2010_percent) #0-14
Out[3147]:
In [3148]:
total_male_percent_2010= (s10_2/s10_1)*100
total_male_percent_2010
Out[3148]:
In [3149]:
total_female_percent_2010= (s10_3/s10_1)*100
total_female_percent_2010
Out[3149]:
In [ ]:
2011¶
In [3150]:
df_2011 = suicide_df.loc[(suicide_df["Year"]==2011)]
df_2011
Out[3150]:
In [3151]:
t2011_dcount = df_2011.loc[:,"Total"]
s11_1 = np.sum(t2011_dcount)
s11_1
Out[3151]:
In [3152]:
male_2011 = df_2011.loc[(df_2011["Gender"]=="Male")]
male_2011
Out[3152]:
In [3153]:
m2011_count = male_2011.loc[:,"Total"]
s11_2 = np.sum(m2011_count)
s11_2
Out[3153]:
In [3154]:
female_2011 = df_2011.loc[(df_2011["Gender"]=="Female")]
female_2011
Out[3154]:
In [3155]:
fm2011_count = female_2011.loc[:,"Total"]
s11_3 = np.sum(fm2011_count)
s11_3
Out[3155]:
In [3156]:
df_age_d014_2011 = df_2011.loc[(df_2011["Age_group"]=="0-14")]
df_age_d014_2011
Out[3156]:
In [3157]:
t_014_2011 = df_age_d014_2011.loc[:,"Total"]
s11_4 = np.sum(t_014_2011)
s11_4
Out[3157]:
In [3158]:
df_age_d1529_2011 = df_2011.loc[(df_2011["Age_group"]=="15-29")]
df_age_d1529_2011
Out[3158]:
In [3159]:
t_1529_2011 = df_age_d1529_2011.loc[:,"Total"]
s11_5 = np.sum(t_1529_2011)
s11_5
Out[3159]:
In [3160]:
df_age_d3044_2011 = df_2011.loc[(df_2011["Age_group"]=="30-44")]
df_age_d3044_2011
Out[3160]:
In [3161]:
t_3044_2011 = df_age_d3044_2011.loc[:,"Total"]
s11_6 = np.sum(t_3044_2011)
s11_6
Out[3161]:
In [3162]:
df_age_d4559_2011 = df_2011.loc[(df_2011["Age_group"]=="45-59")]
df_age_d4559_2011
Out[3162]:
In [3163]:
t_4559_2011 = df_age_d4559_2011.loc[:,"Total"]
s11_7 = np.sum(t_4559_2011)
s11_7
Out[3163]:
In [3164]:
df_age_d60_2011 = df_2011.loc[(df_2011["Age_group"]=="60+")]
df_age_d60_2011
Out[3164]:
In [3165]:
t_60_2011 = df_age_d60_2011.loc[:,"Total"]
s11_8 = np.sum(t_60_2011)
s11_8
Out[3165]:
In [3166]:
df_age_d0100_2011 = df_2011.loc[(df_2011["Age_group"]=="0-100+")]
df_age_d0100_2011
Out[3166]:
In [3167]:
t_0100_2011 = df_age_d0100_2011.loc[:,"Total"]
s11_54 = np.sum(t_0100_2011)
s11_54
Out[3167]:
In [3168]:
df_014m_2011 = df_age_d014_2011.loc[(df_age_d014_2011["Gender"]=="Male")]
df_014m_2011
Out[3168]:
In [3169]:
t_014m_2011 = df_014m_2011.loc[:,"Total"]
s11_9 = np.sum(t_014m_2011)
s11_9
Out[3169]:
In [3170]:
df_014fm_2011 = df_age_d014_2011.loc[(df_age_d014_2011["Gender"]=="Female")]
df_014fm_2011
Out[3170]:
In [3171]:
t_014fm_2011 = df_014fm_2011.loc[:,"Total"]
s11_10 = np.sum(t_014fm_2011)
s11_10
Out[3171]:
In [3172]:
df_1529m_2011 = df_age_d1529_2011.loc[(df_age_d1529_2011["Gender"]=="Male")]
df_1529m_2011
Out[3172]:
In [3173]:
t_1529m_2011 = df_1529m_2011.loc[:,"Total"]
s11_11 = np.sum(t_1529m_2011)
s11_11
Out[3173]:
In [3174]:
df_1529fm_2011 = df_age_d1529_2011.loc[(df_age_d1529_2011["Gender"]=="Female")]
df_1529fm_2011
Out[3174]:
In [3175]:
t_1529fm_2011 = df_1529fm_2011.loc[:,"Total"]
s11_12 = np.sum(t_1529fm_2011)
s11_12
Out[3175]:
In [3176]:
df_3044m_2011 = df_age_d3044_2011.loc[(df_age_d3044_2011["Gender"]=="Male")]
df_3044m_2011
Out[3176]:
In [3177]:
t_3044m_2011 = df_3044m_2011.loc[:,"Total"]
s11_13 = np.sum(t_3044m_2011)
s11_13
Out[3177]:
In [3178]:
df_3044fm_2011 = df_age_d3044_2011.loc[(df_age_d3044_2011["Gender"]=="Female")]
df_3044fm_2011
Out[3178]:
In [3179]:
t_3044fm_2011 = df_3044fm_2011.loc[:,"Total"]
s11_14 = np.sum(t_3044fm_2011)
s11_14
Out[3179]:
In [3180]:
df_4559m_2011 = df_age_d4559_2011.loc[(df_age_d4559_2011["Gender"]=="Male")]
df_4559m_2011
Out[3180]:
In [3181]:
t_4559m_2011 = df_4559m_2011.loc[:,"Total"]
s11_15 = np.sum(t_4559m_2011)
s11_15
Out[3181]:
In [3182]:
df_4559fm_2011 = df_age_d4559_2011.loc[(df_age_d4559_2011["Gender"]=="Female")]
df_4559fm_2011
Out[3182]:
In [3183]:
t_4559fm_2011 = df_4559fm_2011.loc[:,"Total"]
s11_16 = np.sum(t_4559fm_2011)
s11_16
Out[3183]:
In [3184]:
df_60m_2011 = df_age_d60_2011.loc[(df_age_d60_2011["Gender"]=="Male")]
df_60m_2011
Out[3184]:
In [3185]:
t_60m_2011 = df_60m_2011.loc[:,"Total"]
s11_17 = np.sum(t_60m_2011)
s11_17
Out[3185]:
In [3186]:
df_60fm_2011 = df_age_d60_2011.loc[(df_age_d60_2011["Gender"]=="Female")]
df_60fm_2011
Out[3186]:
In [3187]:
t_60fm_2011 = df_60fm_2011.loc[:,"Total"]
s11_18 = np.sum(t_60fm_2011)
s11_18
Out[3187]:
In [3188]:
df_0100fm_2011 = df_age_d0100_2011.loc[(df_age_d0100_2011["Gender"]=="Female")]
df_0100fm_2011
Out[3188]:
In [3189]:
t_0100fm_2011 = df_0100fm_2011.loc[:,"Total"]
s11_55 = np.sum(t_0100fm_2011)
s11_55
Out[3189]:
In [3190]:
df_0100m_2011 = df_age_d0100_2011.loc[(df_age_d0100_2011["Gender"]=="Male")]
df_0100m_2011
Out[3190]:
In [3191]:
t_0100m_2011 = df_0100m_2011.loc[:,"Total"]
s11_56 = np.sum(t_0100m_2011)
s11_56
Out[3191]:
In [3192]:
df_st_AN_2011 = df_2011.loc[(df_2011["State"]=="A & N ISLANDS")]
df_st_AN_2011
Out[3192]:
In [3193]:
t_AN_2011 = df_st_AN_2011.loc[:,"Total"]
s11_19 = np.sum(t_AN_2011)
s11_19
Out[3193]:
In [3194]:
df_st_AP_2011 = df_2011.loc[(df_2011["State"]=="ANDHRA PRADESH")]
df_st_AP_2011
Out[3194]:
In [3195]:
t_AP_2011 = df_st_AP_2011.loc[:,"Total"]
s11_20 = np.sum(t_AP_2011)
s11_20
Out[3195]:
In [3196]:
df_st_ARP_2011 = df_2011.loc[(df_2011["State"]=="ARUNACHAL PRADESH")]
df_st_ARP_2011
Out[3196]:
In [3197]:
t_ARP_2011 = df_st_ARP_2011.loc[:,"Total"]
s11_21 = np.sum(t_ARP_2011)
s11_21
Out[3197]:
In [3198]:
df_st_AS_2011 = df_2011.loc[(df_2011["State"]=="ASSAM")]
df_st_AS_2011
Out[3198]:
In [3199]:
t_AS_2011 = df_st_AS_2011.loc[:,"Total"]
s11_22 = np.sum(t_AS_2011)
s11_22
Out[3199]:
In [3200]:
df_st_BH_2011 = df_2011.loc[(df_2011["State"]=="BIHAR")]
df_st_BH_2011
Out[3200]:
In [3201]:
t_BH_2011 = df_st_BH_2011.loc[:,"Total"]
s11_23 = np.sum(t_BH_2011)
s11_23
Out[3201]:
In [3202]:
df_st_CH_2011 = df_2011.loc[(df_2011["State"]=="CHANDIGARH")]
df_st_CH_2011
Out[3202]:
In [3203]:
t_CH_2011 = df_st_CH_2011.loc[:,"Total"]
s11_24 = np.sum(t_CH_2011)
s11_24
Out[3203]:
In [3204]:
df_st_CT_2011 = df_2011.loc[(df_2011["State"]=="CHHATTISGARH")]
df_st_CT_2011
Out[3204]:
In [3205]:
t_CT_2011 = df_st_CT_2011.loc[:,"Total"]
s11_25 = np.sum(t_CT_2011)
s11_25
Out[3205]:
In [3206]:
df_st_DN_2011 = df_2011.loc[(df_2011["State"]=="D & N HAVELI")]
df_st_DN_2011
Out[3206]:
In [3207]:
t_DN_2011 = df_st_DN_2011.loc[:,"Total"]
s11_26 = np.sum(t_DN_2011)
s11_26
Out[3207]:
In [3208]:
df_st_DD_2011 = df_2011.loc[(df_2011["State"]=="DAMAN & DIU")]
df_st_DD_2011
Out[3208]:
In [3209]:
t_DD_2011 = df_st_DD_2011.loc[:,"Total"]
s11_27 = np.sum(t_DD_2011)
s11_27
Out[3209]:
In [3210]:
df_st_DL_2011 = df_2011.loc[(df_2011["State"]=="DELHI (UT)")]
df_st_DL_2011
Out[3210]:
In [3211]:
t_DL_2011 = df_st_DL_2011.loc[:,"Total"]
s11_28 = np.sum(t_DL_2011)
s11_28
Out[3211]:
In [3212]:
df_st_GOA_2011 = df_2011.loc[(df_2011["State"]=="GOA")]
df_st_GOA_2011
Out[3212]:
In [3213]:
t_GOA_2011 = df_st_GOA_2011.loc[:,"Total"]
s11_29 = np.sum(t_GOA_2011)
s11_29
Out[3213]:
In [3214]:
df_st_GJ_2011 = df_2011.loc[(df_2011["State"]=="GUJARAT")]
df_st_GJ_2011
Out[3214]:
In [3215]:
t_GJ_2011 = df_st_GJ_2011.loc[:,"Total"]
s11_30 = np.sum(t_GJ_2011)
s11_30
Out[3215]:
In [3216]:
df_st_HR_2011 = df_2011.loc[(df_2011["State"]=="HARYANA")]
df_st_HR_2011
Out[3216]:
In [3217]:
t_HR_2011 = df_st_HR_2011.loc[:,"Total"]
s11_31 = np.sum(t_HR_2011)
s11_31
Out[3217]:
In [3218]:
df_st_HP_2011 = df_2011.loc[(df_2011["State"]=="HIMACHAL PRADESH")]
df_st_HP_2011
Out[3218]:
In [3219]:
t_HP_2011 = df_st_HP_2011.loc[:,"Total"]
s11_32 = np.sum(t_HP_2011)
s11_32
Out[3219]:
In [3220]:
df_st_JK_2011 = df_2011.loc[(df_2011["State"]=="JAMMU & KASHMIR")]
df_st_JK_2011
Out[3220]:
In [3221]:
t_JK_2011 = df_st_JK_2011.loc[:,"Total"]
s11_33 = np.sum(t_JK_2011)
s11_33
Out[3221]:
In [3222]:
df_st_JH_2011 = df_2011.loc[(df_2011["State"]=="JHARKHAND")]
df_st_JH_2011
Out[3222]:
In [3223]:
t_JH_2011 = df_st_JH_2011.loc[:,"Total"]
s11_34 = np.sum(t_JH_2011)
s11_34
Out[3223]:
In [3224]:
df_st_KN_2011 = df_2011.loc[(df_2011["State"]=="KARNATAKA")]
df_st_KN_2011
Out[3224]:
In [3225]:
t_KN_2011 = df_st_KN_2011.loc[:,"Total"]
s11_35 = np.sum(t_KN_2011)
s11_35
Out[3225]:
In [3226]:
df_st_KER_2011 = df_2011.loc[(df_2011["State"]=="KERALA")]
df_st_KER_2011
Out[3226]:
In [3227]:
t_KER_2011 = df_st_KER_2011.loc[:,"Total"]
s11_36 = np.sum(t_KER_2011)
s11_36
Out[3227]:
In [3228]:
df_st_LD_2011 = df_2011.loc[(df_2011["State"]=="LAKSHADWEEP")]
df_st_LD_2011
Out[3228]:
In [3229]:
t_LD_2011 = df_st_LD_2011.loc[:,"Total"]
s11_37 = np.sum(t_LD_2011)
s11_37
Out[3229]:
In [3230]:
df_st_MP_2011 = df_2011.loc[(df_2011["State"]=="MADHYA PRADESH")]
df_st_MP_2011
Out[3230]:
In [3231]:
t_MP_2011 = df_st_MP_2011.loc[:,"Total"]
s11_38 = np.sum(t_MP_2011)
s11_38
Out[3231]:
In [3232]:
df_st_MH_2011 = df_2011.loc[(df_2011["State"]=="MAHARASHTRA")]
df_st_MH_2011
Out[3232]:
In [3233]:
t_MH_2011 = df_st_MH_2011.loc[:,"Total"]
s11_39 = np.sum(t_MH_2011)
s11_39
Out[3233]:
In [3234]:
df_st_MN_2011 = df_2011.loc[(df_2011["State"]=="MANIPUR")]
df_st_MN_2011
Out[3234]:
In [3235]:
t_MN_2011 = df_st_MN_2011.loc[:,"Total"]
s11_40 = np.sum(t_MN_2011)
s11_40
Out[3235]:
In [3236]:
df_st_MG_2011 = df_2011.loc[(df_2011["State"]=="MEGHALAYA")]
df_st_MG_2011
Out[3236]:
In [3237]:
t_MG_2011 = df_st_MG_2011.loc[:,"Total"]
s11_41 = np.sum(t_MG_2011)
s11_41
Out[3237]:
In [3238]:
df_st_MZ_2011 = df_2011.loc[(df_2011["State"]=="MIZORAM")]
df_st_MZ_2011
Out[3238]:
In [3239]:
t_MN_2011 = df_st_MN_2011.loc[:,"Total"]
s11_42 = np.sum(t_MN_2011)
s11_42
Out[3239]:
In [3240]:
df_st_NG_2011 = df_2011.loc[(df_2011["State"]=="NAGALAND")]
df_st_NG_2011
Out[3240]:
In [3241]:
t_NG_2011 = df_st_NG_2011.loc[:,"Total"]
s11_43 = np.sum(t_NG_2011)
s11_43
Out[3241]:
In [3242]:
df_st_OD_2011 = df_2011.loc[(df_2011["State"]=="ODISHA")]
df_st_OD_2011
Out[3242]:
In [3243]:
t_OD_2011 = df_st_OD_2011.loc[:,"Total"]
s11_44 = np.sum(t_OD_2011)
s11_44
Out[3243]:
In [3244]:
df_st_PD_2011 = df_2011.loc[(df_2011["State"]=="PUDUCHERRY")]
df_st_PD_2011
Out[3244]:
In [3245]:
t_PD_2011 = df_st_PD_2011.loc[:,"Total"]
s11_45 = np.sum(t_PD_2011)
s11_45
Out[3245]:
In [3246]:
df_st_PB_2011 = df_2011.loc[(df_2011["State"]=="PUNJAB")]
df_st_PB_2011
Out[3246]:
In [3247]:
t_PB_2011 = df_st_PB_2011.loc[:,"Total"]
s11_46 = np.sum(t_PB_2011)
s11_46
Out[3247]:
In [3248]:
df_st_RJ_2011 = df_2011.loc[(df_2011["State"]=="RAJASTHAN")]
df_st_RJ_2011
Out[3248]:
In [3249]:
t_RJ_2011 = df_st_RJ_2011.loc[:,"Total"]
s11_47 = np.sum(t_RJ_2011)
s11_47
Out[3249]:
In [3250]:
df_st_SM_2011 = df_2011.loc[(df_2011["State"]=="SIKKIM")]
df_st_SM_2011
Out[3250]:
In [3251]:
t_SM_2011 = df_st_SM_2011.loc[:,"Total"]
s11_48 = np.sum(t_SM_2011)
s11_48
Out[3251]:
In [3252]:
df_st_TN_2011 = df_2011.loc[(df_2011["State"]=="TAMIL NADU")]
df_st_TN_2011
Out[3252]:
In [3253]:
t_TN_2011 = df_st_TN_2011.loc[:,"Total"]
s11_49 = np.sum(t_TN_2011)
s11_49
Out[3253]:
In [3254]:
df_st_TP_2011 = df_2011.loc[(df_2011["State"]=="TRIPURA")]
df_st_TP_2011
Out[3254]:
In [3255]:
t_TP_2011 = df_st_TP_2011.loc[:,"Total"]
s11_50 = np.sum(t_TP_2011)
s11_50
Out[3255]:
In [3256]:
df_st_UP_2011 = df_2011.loc[(df_2011["State"]=="UTTAR PRADESH")]
df_st_UP_2011
Out[3256]:
In [3257]:
t_UP_2011 = df_st_UP_2011.loc[:,"Total"]
s11_51 = np.sum(t_UP_2011)
s11_51
Out[3257]:
In [3258]:
df_st_UK_2011 = df_2011.loc[(df_2011["State"]=="UTTARAKHAND")]
df_st_UK_2011
Out[3258]:
In [3259]:
t_UK_2011 = df_st_UK_2011.loc[:,"Total"]
s11_52 = np.sum(t_UK_2011)
s11_52
Out[3259]:
In [3260]:
df_st_WB_2011 = df_2011.loc[(df_2011["State"]=="WEST BENGAL")]
df_st_WB_2011
Out[3260]:
In [3261]:
t_WB_2011 = df_st_WB_2011.loc[:,"Total"]
s11_53 = np.sum(t_WB_2011)
s11_53
Out[3261]:
In [3262]:
t_ANm_2011 = np.sum(df_st_AN_2011.where(df_st_AN_2011["Gender"]=="Male")["Total"])
t_ANm_2011
Out[3262]:
In [3263]:
t_ANfm_2011 = np.sum(df_st_AN_2011.where(df_st_AN_2011["Gender"]=="Female")["Total"])
t_ANfm_2011
Out[3263]:
In [3264]:
t_APm_2011 = np.sum(df_st_AP_2011.where(df_st_AP_2011["Gender"]=="Male")["Total"])
t_APm_2011
Out[3264]:
In [3265]:
t_APfm_2011 = np.sum(df_st_AP_2011.where(df_st_AP_2011["Gender"]=="Female")["Total"])
t_APfm_2011
Out[3265]:
In [3266]:
t_ARPm_2011 = np.sum(df_st_ARP_2011.where(df_st_ARP_2011["Gender"]=="Male")["Total"])
t_ARPm_2011
Out[3266]:
In [3267]:
t_ARPfm_2011 = np.sum(df_st_ARP_2011.where(df_st_ARP_2011["Gender"]=="Female")["Total"])
t_ARPfm_2011
Out[3267]:
In [3268]:
t_ASm_2011 = np.sum(df_st_AS_2011.where(df_st_AS_2011["Gender"]=="Male")["Total"])
t_ASm_2011
Out[3268]:
In [3269]:
t_ASfm_2011 = np.sum(df_st_AS_2011.where(df_st_AS_2011["Gender"]=="Female")["Total"])
t_ASfm_2011
Out[3269]:
In [3270]:
t_BHm_2011 = np.sum(df_st_BH_2011.where(df_st_BH_2011["Gender"]=="Male")["Total"])
t_BHm_2011
Out[3270]:
In [3271]:
t_BHfm_2011 = np.sum(df_st_BH_2011.where(df_st_BH_2011["Gender"]=="Female")["Total"])
t_BHfm_2011
Out[3271]:
In [3272]:
t_CHm_2011 = np.sum(df_st_CH_2011.where(df_st_CH_2011["Gender"]=="Male")["Total"])
t_CHm_2011
Out[3272]:
In [3273]:
t_CHfm_2011 = np.sum(df_st_CH_2011.where(df_st_CH_2011["Gender"]=="Female")["Total"])
t_CHfm_2011
Out[3273]:
In [3274]:
t_CTm_2011 = np.sum(df_st_CT_2011.where(df_st_CT_2011["Gender"]=="Male")["Total"])
t_CTm_2011
Out[3274]:
In [3275]:
t_CTfm_2011 = np.sum(df_st_CT_2011.where(df_st_CT_2011["Gender"]=="Female")["Total"])
t_CTfm_2011
Out[3275]:
In [3276]:
t_DNm_2011 = np.sum(df_st_DN_2011.where(df_st_DN_2011["Gender"]=="Male")["Total"])
t_DNm_2011
Out[3276]:
In [3277]:
t_DNfm_2011 = np.sum(df_st_DN_2011.where(df_st_DN_2011["Gender"]=="Female")["Total"])
t_DNfm_2011
Out[3277]:
In [3278]:
t_DDm_2011 = np.sum(df_st_DD_2011.where(df_st_DD_2011["Gender"]=="Male")["Total"])
t_DDm_2011
Out[3278]:
In [3279]:
t_DDfm_2011 = np.sum(df_st_DD_2011.where(df_st_DD_2011["Gender"]=="Female")["Total"])
t_DDfm_2011
Out[3279]:
In [3280]:
t_DLm_2011 = np.sum(df_st_DL_2011.where(df_st_DL_2011["Gender"]=="Male")["Total"])
t_DLm_2011
Out[3280]:
In [3281]:
t_DLfm_2011 = np.sum(df_st_DL_2011.where(df_st_DL_2011["Gender"]=="Female")["Total"])
t_DLfm_2011
Out[3281]:
In [3282]:
t_GOAm_2011 = np.sum(df_st_GOA_2011.where(df_st_GOA_2011["Gender"]=="Male")["Total"])
t_GOAm_2011
Out[3282]:
In [3283]:
t_GOAfm_2011 = np.sum(df_st_GOA_2011.where(df_st_GOA_2011["Gender"]=="Female")["Total"])
t_GOAfm_2011
Out[3283]:
In [3284]:
t_GJm_2011 = np.sum(df_st_GJ_2011.where(df_st_GJ_2011["Gender"]=="Male")["Total"])
t_GJm_2011
Out[3284]:
In [3285]:
t_GJfm_2011 = np.sum(df_st_GJ_2011.where(df_st_GJ_2011["Gender"]=="Female")["Total"])
t_GJfm_2011
Out[3285]:
In [3286]:
t_HRm_2011 = np.sum(df_st_HR_2011.where(df_st_HR_2011["Gender"]=="Male")["Total"])
t_HRm_2011
Out[3286]:
In [3287]:
t_HRfm_2011 = np.sum(df_st_HR_2011.where(df_st_HR_2011["Gender"]=="Female")["Total"])
t_HRfm_2011
Out[3287]:
In [3288]:
t_HPm_2011 = np.sum(df_st_HP_2011.where(df_st_HP_2011["Gender"]=="Male")["Total"])
t_HPm_2011
Out[3288]:
In [3289]:
t_HPfm_2011 = np.sum(df_st_HP_2011.where(df_st_HP_2011["Gender"]=="Female")["Total"])
t_HPfm_2011
Out[3289]:
In [3290]:
t_JKm_2011 = np.sum(df_st_JK_2011.where(df_st_JK_2011["Gender"]=="Male")["Total"])
t_JKm_2011
Out[3290]:
In [3291]:
t_JKfm_2011 = np.sum(df_st_JK_2011.where(df_st_JK_2011["Gender"]=="Female")["Total"])
t_JKfm_2011
Out[3291]:
In [3292]:
t_JHm_2011 = np.sum(df_st_JH_2011.where(df_st_JH_2011["Gender"]=="Male")["Total"])
t_JHm_2011
Out[3292]:
In [3562]:
t_JHfm_2011 = np.sum(df_st_JH_2011.where(df_st_JH_2011["Gender"]=="Female")["Total"])
t_JHfm_2011
Out[3562]:
In [3563]:
t_KNm_2011 = np.sum(df_st_KN_2011.where(df_st_KN_2011["Gender"]=="Male")["Total"])
t_KNm_2011
Out[3563]:
In [3564]:
t_KNfm_2011 = np.sum(df_st_KN_2011.where(df_st_KN_2011["Gender"]=="Female")["Total"])
t_KNfm_2011
Out[3564]:
In [3565]:
t_KERm_2011 = np.sum(df_st_KER_2011.where(df_st_KER_2011["Gender"]=="Male")["Total"])
t_KERm_2011
Out[3565]:
In [3566]:
t_KERfm_2011 = np.sum(df_st_KER_2011.where(df_st_KER_2011["Gender"]=="Female")["Total"])
t_KERfm_2011
Out[3566]:
In [3567]:
t_LDm_2011 = np.sum(df_st_LD_2011.where(df_st_LD_2011["Gender"]=="Male")["Total"])
t_LDm_2011
Out[3567]:
In [3568]:
t_LDfm_2011 = np.sum(df_st_LD_2011.where(df_st_LD_2011["Gender"]=="Female")["Total"])
t_LDfm_2011
Out[3568]:
In [3569]:
t_MPm_2011 = np.sum(df_st_MP_2011.where(df_st_MP_2011["Gender"]=="Male")["Total"])
t_MPm_2011
Out[3569]:
In [3570]:
t_MPfm_2011 = np.sum(df_st_MP_2011.where(df_st_MP_2011["Gender"]=="Female")["Total"])
t_MPfm_2011
Out[3570]:
In [3571]:
t_MHm_2011 = np.sum(df_st_MH_2011.where(df_st_MH_2011["Gender"]=="Male")["Total"])
t_MHm_2011
Out[3571]:
In [3572]:
t_MHfm_2011 = np.sum(df_st_MH_2011.where(df_st_MH_2011["Gender"]=="Female")["Total"])
t_MHfm_2011
Out[3572]:
In [3573]:
t_MNm_2011 = np.sum(df_st_MN_2011.where(df_st_MN_2011["Gender"]=="Male")["Total"])
t_MNm_2011
Out[3573]:
In [3574]:
t_MNfm_2011 = np.sum(df_st_MN_2011.where(df_st_MN_2011["Gender"]=="Female")["Total"])
t_MNfm_2011
Out[3574]:
In [3575]:
t_MGm_2011 = np.sum(df_st_MG_2011.where(df_st_MG_2011["Gender"]=="Male")["Total"])
t_MGm_2011
Out[3575]:
In [3576]:
t_MGfm_2011 = np.sum(df_st_MG_2011.where(df_st_MG_2011["Gender"]=="Female")["Total"])
t_MGfm_2011
Out[3576]:
In [3577]:
t_MZm_2011 = np.sum(df_st_MZ_2011.where(df_st_MZ_2011["Gender"]=="Male")["Total"])
t_MZm_2011
Out[3577]:
In [3578]:
t_MZfm_2011 = np.sum(df_st_MZ_2011.where(df_st_MZ_2011["Gender"]=="Female")["Total"])
t_MZfm_2011
Out[3578]:
In [3579]:
t_NGm_2011 = np.sum(df_st_NG_2011.where(df_st_NG_2011["Gender"]=="Male")["Total"])
t_NGm_2011
Out[3579]:
In [3580]:
t_NGfm_2011 = np.sum(df_st_NG_2011.where(df_st_NG_2011["Gender"]=="Female")["Total"])
t_MGfm_2011
Out[3580]:
In [3581]:
t_ODm_2011 = np.sum(df_st_OD_2011.where(df_st_OD_2011["Gender"]=="Male")["Total"])
t_ODm_2011
Out[3581]:
In [3582]:
t_ODfm_2011 = np.sum(df_st_OD_2011.where(df_st_OD_2011["Gender"]=="Female")["Total"])
t_ODfm_2011
Out[3582]:
In [3583]:
t_PDm_2011 = np.sum(df_st_PD_2011.where(df_st_PD_2011["Gender"]=="Male")["Total"])
t_PDm_2011
Out[3583]:
In [3584]:
t_PDfm_2011 = np.sum(df_st_PD_2011.where(df_st_PD_2011["Gender"]=="Female")["Total"])
t_PDfm_2011
Out[3584]:
In [3585]:
t_PBm_2011 = np.sum(df_st_PB_2011.where(df_st_PB_2011["Gender"]=="Male")["Total"])
t_PBm_2011
Out[3585]:
In [3586]:
t_PBfm_2011 = np.sum(df_st_PB_2011.where(df_st_PB_2011["Gender"]=="Female")["Total"])
t_PBfm_2011
Out[3586]:
In [3587]:
t_RJm_2011 = np.sum(df_st_RJ_2011.where(df_st_RJ_2011["Gender"]=="Male")["Total"])
t_RJm_2011
Out[3587]:
In [3588]:
t_RJfm_2011 = np.sum(df_st_RJ_2011.where(df_st_RJ_2011["Gender"]=="Female")["Total"])
t_RJfm_2011
Out[3588]:
In [3589]:
t_SMm_2011 = np.sum(df_st_SM_2011.where(df_st_SM_2011["Gender"]=="Male")["Total"])
t_SMm_2011
Out[3589]:
In [3590]:
t_SMfm_2011 = np.sum(df_st_SM_2011.where(df_st_SM_2011["Gender"]=="Female")["Total"])
t_SMfm_2011
Out[3590]:
In [3591]:
t_TNm_2011 = np.sum(df_st_TN_2011.where(df_st_TN_2011["Gender"]=="Male")["Total"])
t_TNm_2011
Out[3591]:
In [3592]:
t_TNfm_2011 = np.sum(df_st_TN_2011.where(df_st_TN_2011["Gender"]=="Female")["Total"])
t_TNfm_2011
Out[3592]:
In [3593]:
t_TPm_2011 = np.sum(df_st_TP_2011.where(df_st_TP_2011["Gender"]=="Male")["Total"])
t_TPm_2011
Out[3593]:
In [3594]:
t_TPfm_2011 = np.sum(df_st_TP_2011.where(df_st_TP_2011["Gender"]=="Female")["Total"])
t_TPfm_2011
Out[3594]:
In [3595]:
t_UPm_2011 = np.sum(df_st_UP_2011.where(df_st_UP_2011["Gender"]=="Male")["Total"])
t_UPm_2011
Out[3595]:
In [3596]:
t_UPfm_2011 = np.sum(df_st_UP_2011.where(df_st_UP_2011["Gender"]=="Female")["Total"])
t_UPfm_2011
Out[3596]:
In [3597]:
t_UKm_2011 = np.sum(df_st_UK_2011.where(df_st_UK_2011["Gender"]=="Male")["Total"])
t_UKm_2011
Out[3597]:
In [3598]:
t_UKfm_2011 = np.sum(df_st_UK_2011.where(df_st_UK_2011["Gender"]=="Female")["Total"])
t_UKfm_2011
Out[3598]:
In [3599]:
t_WBm_2011 = np.sum(df_st_WB_2011.where(df_st_WB_2011["Gender"]=="Male")["Total"])
t_WBm_2011
Out[3599]:
In [3600]:
t_WBfm_2011 = np.sum(df_st_WB_2011.where(df_st_WB_2011["Gender"]=="Female")["Total"])
t_WBfm_2011
Out[3600]:
In [3601]:
l_2011=list(df_2011.loc[:,"Type"].unique())
l_2011.sort()
print(l_2011,end=" ")
In [3602]:
cause_l_2011=[]
for i in l_2011:
rough= np.sum(df_2011.where(df_2011["Type"]==i)["Total"])
cause_l_2011.append(rough)
print(cause_l_2011,end=" ")
In [3603]:
x1_2011 = set(df_2011.loc[:,"State"])
x1_2011
len(x1_2011)
Out[3603]:
In [3604]:
cause_df_2011 = pd.DataFrame(cause_l_2011,l_2011)
cause_df_2011.reset_index()
Out[3604]:
In [3605]:
cause_df_2011.plot.bar(stacked=True,figsize=(50,20))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("Causes",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths due to Different Causes in 2011",fontsize=55)
plt.legend(["Death Count"], prop={"size":30})
Out[3605]:
In [3606]:
s_2011=list(df_2011.loc[:,"State"].unique())
s_2011.sort()
print(s_2011,end=" ")
In [3607]:
state_s_2011 = [s11_19,s11_20,s11_21,s11_22,s11_23,s11_24,s11_25,s11_26,s11_27,s11_28,s11_29,s11_30,s11_31,s11_32,s11_33,s11_34,
s11_35,s11_36,s11_37,s11_38,s11_39,s11_40,s11_41,s11_42,s11_43,s11_44,s11_45,s11_46,s11_47,s11_48,s11_49,s11_50,
s11_51,s11_52,s11_53]
print(state_s_2011,end=" ")
In [3608]:
state_m_2011=[t_ANm_2011, t_APm_2011, t_ARPm_2011, t_ASm_2011, t_BHm_2011, t_CHm_2011, t_CTm_2011, t_DNm_2011,
t_DDm_2011, t_DLm_2011, t_GOAm_2011, t_GJm_2011, t_HRm_2011, t_HPm_2011,t_JKm_2011, t_JHm_2011,
t_KNm_2011, t_KERm_2011, t_LDm_2011,t_MPm_2011,t_MHm_2011,t_MNm_2011,t_MGm_2011,t_MZm_2011,
t_NGm_2011, t_ODm_2011, t_PDm_2011,t_PBm_2011,t_RJm_2011,t_SMm_2011,t_TNm_2011,t_TPm_2011,
t_UPm_2011, t_UKm_2011, t_WBm_2011]
print(state_m_2011,end=" ")
In [3609]:
state_fm_2011=[t_ANfm_2011, t_APfm_2011, t_ARPfm_2011, t_ASfm_2011, t_BHfm_2011, t_CHfm_2011, t_CTfm_2011, t_DNfm_2011,
t_DDfm_2011, t_DLfm_2011, t_GOAfm_2011, t_GJfm_2011, t_HRfm_2011, t_HPfm_2011,t_JKfm_2011, t_JHfm_2011,
t_KNfm_2011, t_KERfm_2011, t_LDfm_2011,t_MPfm_2011,t_MHfm_2011,t_MNfm_2011,t_MGfm_2011,t_MZfm_2011,
t_NGfm_2011, t_ODfm_2011, t_PDfm_2011,t_PBfm_2011,t_RJfm_2011,t_SMfm_2011,t_TNfm_2011,t_TPfm_2011,
t_UPfm_2011, t_UKfm_2011, t_WBfm_2011]
print(state_fm_2011,end=" ")
In [3610]:
df_state_2011 = {
"State":['A & N ISLANDS', 'ANDHRA PRADESH', 'ARUNACHAL PRADESH', 'ASSAM', 'BIHAR', 'CHANDIGARH', 'CHHATTISGARH',
'D & N HAVELI', 'DAMAN & DIU', 'DELHI (UT)', 'GOA', 'GUJARAT', 'HARYANA', 'HIMACHAL PRADESH', 'JAMMU & KASHMIR',
'JHARKHAND', 'KARNATAKA', 'KERALA', 'LAKSHADWEEP', 'MADHYA PRADESH', 'MAHARASHTRA', 'MANIPUR', 'MEGHALAYA',
'MIZORAM', 'NAGALAND', 'ODISHA', 'PUDUCHERRY', 'PUNJAB', 'RAJASTHAN', 'SIKKIM', 'TAMIL NADU', 'TRIPURA',
'UTTAR PRADESH', 'UTTARAKHAND', 'WEST BENGAL'],
"Total":[s11_19,s11_20,s11_21,s11_22,s11_23,s11_24,s11_25,s11_26,s11_27,s11_28,s11_29,s11_30,s11_31,s11_32,s11_33,s11_34,
s11_35,s11_36,s11_37,s11_38,s11_39,s11_40,s11_41,s11_42,s11_43,s11_44,s11_45,s11_46,s11_47,s11_48,s11_49,s11_50,
s11_51,s11_52,s11_53],
"Male":[t_ANm_2011, t_APm_2011, t_ARPm_2011, t_ASm_2011, t_BHm_2011, t_CHm_2011, t_CTm_2011, t_DNm_2011,
t_DDm_2011, t_DLm_2011, t_GOAm_2011, t_GJm_2011, t_HRm_2011, t_HPm_2011,t_JKm_2011, t_JHm_2011,
t_KNm_2011, t_KERm_2011, t_LDm_2011,t_MPm_2011,t_MHm_2011,t_MNm_2011,t_MGm_2011,t_MZm_2011,
t_NGm_2011, t_ODm_2011, t_PDm_2011,t_PBm_2011,t_RJm_2011,t_SMm_2011,t_TNm_2011,t_TPm_2011,
t_UPm_2011, t_UKm_2011, t_WBm_2011],
"Female":[t_ANfm_2011, t_APfm_2011, t_ARPfm_2011, t_ASfm_2011, t_BHfm_2011, t_CHfm_2011, t_CTfm_2011, t_DNfm_2011,
t_DDfm_2011, t_DLfm_2011, t_GOAfm_2011, t_GJfm_2011, t_HRfm_2011, t_HPfm_2011,t_JKfm_2011, t_JHfm_2011,
t_KNfm_2011, t_KERfm_2011, t_LDfm_2011,t_MPfm_2011,t_MHfm_2011,t_MNfm_2011,t_MGfm_2011,t_MZfm_2011,
t_NGfm_2011, t_ODfm_2011, t_PDfm_2011,t_PBfm_2011,t_RJfm_2011,t_SMfm_2011,t_TNfm_2011,t_TPfm_2011,
t_UPfm_2011, t_UKfm_2011, t_WBfm_2011]
}
df_All_st_2011 = pd.DataFrame(df_state_2011)
df_All_st_2011
Out[3610]:
In [3611]:
pivot_2011 = pd.pivot_table(df_All_st_2011, values=None, index='State', columns=None,
fill_value=None, margins=False, dropna=True, margins_name='All', observed=False)
pivot_2011
Out[3611]:
In [3612]:
pivot_2011.plot(kind='bar',figsize=(60,30))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("States",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths of Males And Females In Different State",fontsize=55)
plt.legend(["Female","Male","Total"], prop={"size":30})
plt.show
Out[3612]:
In [3613]:
cause_l_percent_2011=[]
for i in cause_l_2011:
p=i/np.sum(cause_l_2011)
avg_2011_cause=p*100
cause_l_percent_2011.append(avg_2011_cause)
print(cause_l_percent_2011,end=" ")
In [3614]:
np.max(cause_l_percent_2011) #Married
Out[3614]:
In [3615]:
np.min(cause_l_percent_2011) #Ideological Causes/Hero Worshipping
Out[3615]:
In [3616]:
state_s_2011_percent=[]
tot=np.sum(state_s_2011)
for i in state_s_2011:
xy=i/tot
avg_state_2011=xy*100
state_s_2011_percent.append(avg_state_2011)
print(state_s_2011_percent,end=" ")
In [3617]:
np.max(state_s_2011_percent) #Maharashtra
Out[3617]:
In [3618]:
np.min(state_s_2011_percent) #Lakshdweep
Out[3618]:
In [3619]:
l_age_grp_2011=[s11_4,s11_5,s11_6,s11_7,s11_8,s11_54]
l_age_grp_2011
Out[3619]:
In [3620]:
l_age_grp_2011_percent=[]
for i in l_age_grp_2011:
yz=i/s11_1
avg_2011_age_grp=yz*100
l_age_grp_2011_percent.append(avg_2011_age_grp)
l_age_grp_2011_percent
Out[3620]:
In [3621]:
np.max(l_age_grp_2011_percent) #0-100+
Out[3621]:
In [3622]:
np.min(l_age_grp_2011_percent) #0-14
Out[3622]:
In [3623]:
total_male_percent_2011= (s11_2/s11_1)*100
total_male_percent_2011
Out[3623]:
In [3624]:
total_female_percent_2011= (s11_3/s11_1)*100
total_female_percent_2011
Out[3624]:
In [ ]:
2012¶
In [3625]:
df_2012 = suicide_df.loc[(suicide_df["Year"]==2012)]
df_2012
Out[3625]:
In [3626]:
t2012_dcount = df_2012.loc[:,"Total"]
s12_1 = np.sum(t2012_dcount)
s12_1
Out[3626]:
In [3627]:
male_2012 = df_2012.loc[(df_2012["Gender"]=="Male")]
male_2012
Out[3627]:
In [3628]:
m2012_count = male_2012.loc[:,"Total"]
s12_2 = np.sum(m2012_count)
s12_2
Out[3628]:
In [3629]:
female_2012 = df_2012.loc[(df_2012["Gender"]=="Female")]
female_2012
Out[3629]:
In [3630]:
fm2012_count = female_2012.loc[:,"Total"]
s12_3 = np.sum(fm2012_count)
s12_3
Out[3630]:
In [3631]:
df_age_d014_2012 = df_2012.loc[(df_2012["Age_group"]=="0-14")]
df_age_d014_2012
Out[3631]:
In [3632]:
t_014_2012 = df_age_d014_2012.loc[:,"Total"]
s12_4 = np.sum(t_014_2012)
s12_4
Out[3632]:
In [3633]:
df_age_d1529_2012 = df_2012.loc[(df_2012["Age_group"]=="15-29")]
df_age_d1529_2012
Out[3633]:
In [3634]:
t_1529_2012 = df_age_d1529_2012.loc[:,"Total"]
s12_5 = np.sum(t_1529_2012)
s12_5
Out[3634]:
In [3635]:
df_age_d3044_2012 = df_2012.loc[(df_2012["Age_group"]=="30-44")]
df_age_d3044_2012
Out[3635]:
In [3636]:
t_3044_2012 = df_age_d3044_2012.loc[:,"Total"]
s12_6 = np.sum(t_3044_2012)
s12_6
Out[3636]:
In [3637]:
df_age_d4559_2012 = df_2012.loc[(df_2012["Age_group"]=="45-59")]
df_age_d4559_2012
Out[3637]:
In [3638]:
t_4559_2012 = df_age_d4559_2012.loc[:,"Total"]
s12_7 = np.sum(t_4559_2012)
s12_7
Out[3638]:
In [3639]:
df_age_d60_2012 = df_2012.loc[(df_2012["Age_group"]=="60+")]
df_age_d60_2012
Out[3639]:
In [3640]:
t_60_2012 = df_age_d60_2012.loc[:,"Total"]
s12_8 = np.sum(t_60_2012)
s12_8
Out[3640]:
In [3641]:
df_age_d0100_2012 = df_2012.loc[(df_2012["Age_group"]=="0-100+")]
df_age_d0100_2012
Out[3641]:
In [3642]:
t_0100_2012 = df_age_d0100_2012.loc[:,"Total"]
s12_54 = np.sum(t_0100_2012)
s12_54
Out[3642]:
In [3643]:
df_014m_2012 = df_age_d014_2012.loc[(df_age_d014_2012["Gender"]=="Male")]
df_014m_2012
Out[3643]:
In [3644]:
t_014m_2012 = df_014m_2012.loc[:,"Total"]
s12_9 = np.sum(t_014m_2012)
s12_9
Out[3644]:
In [3645]:
df_014fm_2012 = df_age_d014_2012.loc[(df_age_d014_2012["Gender"]=="Female")]
df_014fm_2012
Out[3645]:
In [3646]:
t_014fm_2012 = df_014fm_2012.loc[:,"Total"]
s12_10 = np.sum(t_014fm_2012)
s12_10
Out[3646]:
In [3647]:
df_1529m_2012 = df_age_d1529_2012.loc[(df_age_d1529_2012["Gender"]=="Male")]
df_1529m_2012
Out[3647]:
In [3648]:
t_1529m_2012 = df_1529m_2012.loc[:,"Total"]
s12_11 = np.sum(t_1529m_2012)
s12_11
Out[3648]:
In [3649]:
df_1529fm_2012 = df_age_d1529_2012.loc[(df_age_d1529_2012["Gender"]=="Female")]
df_1529fm_2012
Out[3649]:
In [3650]:
t_1529fm_2012 = df_1529fm_2012.loc[:,"Total"]
s12_12 = np.sum(t_1529fm_2012)
s12_12
Out[3650]:
In [3651]:
df_3044m_2012 = df_age_d3044_2012.loc[(df_age_d3044_2012["Gender"]=="Male")]
df_3044m_2012
Out[3651]:
In [3652]:
t_3044m_2012 = df_3044m_2012.loc[:,"Total"]
s12_13 = np.sum(t_3044m_2012)
s12_13
Out[3652]:
In [3653]:
df_3044fm_2012 = df_age_d3044_2012.loc[(df_age_d3044_2012["Gender"]=="Female")]
df_3044fm_2012
Out[3653]:
In [3654]:
t_3044fm_2012 = df_3044fm_2012.loc[:,"Total"]
s12_14 = np.sum(t_3044fm_2012)
s12_14
Out[3654]:
In [3655]:
df_4559m_2012 = df_age_d4559_2012.loc[(df_age_d4559_2012["Gender"]=="Male")]
df_4559m_2012
Out[3655]:
In [3656]:
t_4559m_2012 = df_4559m_2012.loc[:,"Total"]
s12_15 = np.sum(t_4559m_2012)
s12_15
Out[3656]:
In [3657]:
df_4559fm_2012 = df_age_d4559_2012.loc[(df_age_d4559_2012["Gender"]=="Female")]
df_4559fm_2012
Out[3657]:
In [3658]:
t_4559fm_2012 = df_4559fm_2012.loc[:,"Total"]
s12_16 = np.sum(t_4559fm_2012)
s12_16
Out[3658]:
In [3659]:
df_60m_2012 = df_age_d60_2012.loc[(df_age_d60_2012["Gender"]=="Male")]
df_60m_2012
Out[3659]:
In [3660]:
t_60m_2012 = df_60m_2012.loc[:,"Total"]
s12_17 = np.sum(t_60m_2012)
s12_17
Out[3660]:
In [3661]:
df_60fm_2012 = df_age_d60_2012.loc[(df_age_d60_2012["Gender"]=="Female")]
df_60fm_2012
Out[3661]:
In [3662]:
t_60fm_2012 = df_60fm_2012.loc[:,"Total"]
s12_18 = np.sum(t_60fm_2012)
s12_18
Out[3662]:
In [3663]:
df_0100fm_2012 = df_age_d0100_2012.loc[(df_age_d0100_2012["Gender"]=="Female")]
df_0100fm_2012
Out[3663]:
In [3664]:
t_0100fm_2012 = df_0100fm_2012.loc[:,"Total"]
s12_55 = np.sum(t_0100fm_2012)
s12_55
Out[3664]:
In [3665]:
df_0100m_2012 = df_age_d0100_2012.loc[(df_age_d0100_2012["Gender"]=="Male")]
df_0100m_2012
Out[3665]:
In [3666]:
t_0100m_2012 = df_0100m_2012.loc[:,"Total"]
s12_56 = np.sum(t_0100m_2012)
s12_56
Out[3666]:
In [3667]:
df_st_AN_2012 = df_2012.loc[(df_2012["State"]=="A & N ISLANDS")]
df_st_AN_2012
Out[3667]:
In [3668]:
t_AN_2012 = df_st_AN_2012.loc[:,"Total"]
s12_19 = np.sum(t_AN_2012)
s12_19
Out[3668]:
In [3669]:
df_st_AP_2012 = df_2012.loc[(df_2012["State"]=="ANDHRA PRADESH")]
df_st_AP_2012
Out[3669]:
In [3670]:
t_AP_2012 = df_st_AP_2012.loc[:,"Total"]
s12_20 = np.sum(t_AP_2012)
s12_20
Out[3670]:
In [3671]:
df_st_ARP_2012 = df_2012.loc[(df_2012["State"]=="ARUNACHAL PRADESH")]
df_st_ARP_2012
Out[3671]:
In [3672]:
t_ARP_2012 = df_st_ARP_2012.loc[:,"Total"]
s12_21 = np.sum(t_ARP_2012)
s12_21
Out[3672]:
In [3673]:
df_st_AS_2012 = df_2012.loc[(df_2012["State"]=="ASSAM")]
df_st_AS_2012
Out[3673]:
In [3674]:
t_AS_2012 = df_st_AS_2012.loc[:,"Total"]
s12_22 = np.sum(t_AS_2012)
s12_22
Out[3674]:
In [3675]:
df_st_BH_2012 = df_2012.loc[(df_2012["State"]=="BIHAR")]
df_st_BH_2012
Out[3675]:
In [3676]:
t_BH_2012 = df_st_BH_2012.loc[:,"Total"]
s12_23 = np.sum(t_BH_2012)
s12_23
Out[3676]:
In [3677]:
df_st_CH_2012 = df_2012.loc[(df_2012["State"]=="CHANDIGARH")]
df_st_CH_2012
Out[3677]:
In [3678]:
t_CH_2012 = df_st_CH_2012.loc[:,"Total"]
s12_24 = np.sum(t_CH_2012)
s12_24
Out[3678]:
In [3679]:
df_st_CT_2012 = df_2012.loc[(df_2012["State"]=="CHHATTISGARH")]
df_st_CT_2012
Out[3679]:
In [3680]:
t_CT_2012 = df_st_CT_2012.loc[:,"Total"]
s12_25 = np.sum(t_CT_2012)
s12_25
Out[3680]:
In [3681]:
df_st_DN_2012 = df_2012.loc[(df_2012["State"]=="D & N HAVELI")]
df_st_DN_2012
Out[3681]:
In [3682]:
t_DN_2012 = df_st_DN_2012.loc[:,"Total"]
s12_26 = np.sum(t_DN_2012)
s12_26
Out[3682]:
In [3683]:
df_st_DD_2012 = df_2012.loc[(df_2012["State"]=="DAMAN & DIU")]
df_st_DD_2012
Out[3683]:
In [3684]:
t_DD_2012 = df_st_DD_2012.loc[:,"Total"]
s12_27 = np.sum(t_DD_2012)
s12_27
Out[3684]:
In [3685]:
df_st_DL_2012 = df_2012.loc[(df_2012["State"]=="DELHI (UT)")]
df_st_DL_2012
Out[3685]:
In [3686]:
t_DL_2012 = df_st_DL_2012.loc[:,"Total"]
s12_28 = np.sum(t_DL_2012)
s12_28
Out[3686]:
In [3687]:
df_st_GOA_2012 = df_2012.loc[(df_2012["State"]=="GOA")]
df_st_GOA_2012
Out[3687]:
In [3688]:
t_GOA_2012 = df_st_GOA_2012.loc[:,"Total"]
s12_29 = np.sum(t_GOA_2012)
s12_29
Out[3688]:
In [3689]:
df_st_GJ_2012 = df_2012.loc[(df_2012["State"]=="GUJARAT")]
df_st_GJ_2012
Out[3689]:
In [3690]:
t_GJ_2012 = df_st_GJ_2012.loc[:,"Total"]
s12_30 = np.sum(t_GJ_2012)
s12_30
Out[3690]:
In [3691]:
df_st_HR_2012 = df_2012.loc[(df_2012["State"]=="HARYANA")]
df_st_HR_2012
Out[3691]:
In [3692]:
t_HR_2012 = df_st_HR_2012.loc[:,"Total"]
s12_31 = np.sum(t_HR_2012)
s12_31
Out[3692]:
In [3693]:
df_st_HP_2012 = df_2012.loc[(df_2012["State"]=="HIMACHAL PRADESH")]
df_st_HP_2012
Out[3693]:
In [3694]:
t_HP_2012 = df_st_HP_2012.loc[:,"Total"]
s12_32 = np.sum(t_HP_2012)
s12_32
Out[3694]:
In [3695]:
df_st_JK_2012 = df_2012.loc[(df_2012["State"]=="JAMMU & KASHMIR")]
df_st_JK_2012
Out[3695]:
In [3696]:
t_JK_2012 = df_st_JK_2012.loc[:,"Total"]
s12_33 = np.sum(t_JK_2012)
s12_33
Out[3696]:
In [3697]:
df_st_JH_2012 = df_2012.loc[(df_2012["State"]=="JHARKHAND")]
df_st_JH_2012
Out[3697]:
In [3698]:
t_JH_2012 = df_st_JH_2012.loc[:,"Total"]
s12_34 = np.sum(t_JH_2012)
s12_34
Out[3698]:
In [3699]:
df_st_KN_2012 = df_2012.loc[(df_2012["State"]=="KARNATAKA")]
df_st_KN_2012
Out[3699]:
In [3700]:
t_KN_2012 = df_st_KN_2012.loc[:,"Total"]
s12_35 = np.sum(t_KN_2012)
s12_35
Out[3700]:
In [3701]:
df_st_KER_2012 = df_2012.loc[(df_2012["State"]=="KERALA")]
df_st_KER_2012
Out[3701]:
In [3702]:
t_KER_2012 = df_st_KER_2012.loc[:,"Total"]
s12_36 = np.sum(t_KER_2012)
s12_36
Out[3702]:
In [3703]:
df_st_LD_2012 = df_2012.loc[(df_2012["State"]=="LAKSHADWEEP")]
df_st_LD_2012
Out[3703]:
In [3704]:
t_LD_2012 = df_st_LD_2012.loc[:,"Total"]
s12_37 = np.sum(t_LD_2012)
s12_37
Out[3704]:
In [3705]:
df_st_MP_2012 = df_2012.loc[(df_2012["State"]=="MADHYA PRADESH")]
df_st_MP_2012
Out[3705]:
In [3706]:
t_MP_2012 = df_st_MP_2012.loc[:,"Total"]
s12_38 = np.sum(t_MP_2012)
s12_38
Out[3706]:
In [3707]:
df_st_MH_2012 = df_2012.loc[(df_2012["State"]=="MAHARASHTRA")]
df_st_MH_2012
Out[3707]:
In [3708]:
t_MH_2012 = df_st_MH_2012.loc[:,"Total"]
s12_39 = np.sum(t_MH_2012)
s12_39
Out[3708]:
In [3709]:
df_st_MN_2012 = df_2012.loc[(df_2012["State"]=="MANIPUR")]
df_st_MN_2012
Out[3709]:
In [3710]:
t_MN_2012 = df_st_MN_2012.loc[:,"Total"]
s12_40 = np.sum(t_MN_2012)
s12_40
Out[3710]:
In [3711]:
df_st_MG_2012 = df_2012.loc[(df_2012["State"]=="MEGHALAYA")]
df_st_MG_2012
Out[3711]:
In [3712]:
t_MG_2012 = df_st_MG_2012.loc[:,"Total"]
s12_41 = np.sum(t_MG_2012)
s12_41
Out[3712]:
In [3713]:
df_st_MZ_2012 = df_2012.loc[(df_2012["State"]=="MIZORAM")]
df_st_MZ_2012
Out[3713]:
In [3714]:
t_MN_2012 = df_st_MN_2012.loc[:,"Total"]
s12_42 = np.sum(t_MN_2012)
s12_42
Out[3714]:
In [3715]:
df_st_NG_2012 = df_2012.loc[(df_2012["State"]=="NAGALAND")]
df_st_NG_2012
Out[3715]:
In [3716]:
t_NG_2012 = df_st_NG_2012.loc[:,"Total"]
s12_43 = np.sum(t_NG_2012)
s12_43
Out[3716]:
In [3717]:
df_st_OD_2012 = df_2012.loc[(df_2012["State"]=="ODISHA")]
df_st_OD_2012
Out[3717]:
In [3718]:
t_OD_2012 = df_st_OD_2012.loc[:,"Total"]
s12_44 = np.sum(t_OD_2012)
s12_44
Out[3718]:
In [3719]:
df_st_PD_2012 = df_2012.loc[(df_2012["State"]=="PUDUCHERRY")]
df_st_PD_2012
Out[3719]:
In [3720]:
t_PD_2012 = df_st_PD_2012.loc[:,"Total"]
s12_45 = np.sum(t_PD_2012)
s12_45
Out[3720]:
In [3721]:
df_st_PB_2012 = df_2012.loc[(df_2012["State"]=="PUNJAB")]
df_st_PB_2012
Out[3721]:
In [3722]:
t_PB_2012 = df_st_PB_2012.loc[:,"Total"]
s12_46 = np.sum(t_PB_2012)
s12_46
Out[3722]:
In [3723]:
df_st_RJ_2012 = df_2012.loc[(df_2012["State"]=="RAJASTHAN")]
df_st_RJ_2012
Out[3723]:
In [3724]:
t_RJ_2012 = df_st_RJ_2012.loc[:,"Total"]
s12_47 = np.sum(t_RJ_2012)
s12_47
Out[3724]:
In [3725]:
df_st_SM_2012 = df_2012.loc[(df_2012["State"]=="SIKKIM")]
df_st_SM_2012
Out[3725]:
In [3726]:
t_SM_2012 = df_st_SM_2012.loc[:,"Total"]
s12_48 = np.sum(t_SM_2012)
s12_48
Out[3726]:
In [3727]:
df_st_TN_2012 = df_2012.loc[(df_2012["State"]=="TAMIL NADU")]
df_st_TN_2012
Out[3727]:
In [3728]:
t_TN_2012 = df_st_TN_2012.loc[:,"Total"]
s12_49 = np.sum(t_TN_2012)
s12_49
Out[3728]:
In [3729]:
df_st_TP_2012 = df_2012.loc[(df_2012["State"]=="TRIPURA")]
df_st_TP_2012
Out[3729]:
In [3730]:
t_TP_2012 = df_st_TP_2012.loc[:,"Total"]
s12_50 = np.sum(t_TP_2012)
s12_50
Out[3730]:
In [3731]:
df_st_UP_2012 = df_2012.loc[(df_2012["State"]=="UTTAR PRADESH")]
df_st_UP_2012
Out[3731]:
In [3732]:
t_UP_2012 = df_st_UP_2012.loc[:,"Total"]
s12_51 = np.sum(t_UP_2012)
s12_51
Out[3732]:
In [3733]:
df_st_UK_2012 = df_2012.loc[(df_2012["State"]=="UTTARAKHAND")]
df_st_UK_2012
Out[3733]:
In [3734]:
t_UK_2012 = df_st_UK_2012.loc[:,"Total"]
s12_52 = np.sum(t_UK_2012)
s12_52
Out[3734]:
In [3735]:
df_st_WB_2012 = df_2012.loc[(df_2012["State"]=="WEST BENGAL")]
df_st_WB_2012
Out[3735]:
In [3736]:
t_WB_2012 = df_st_WB_2012.loc[:,"Total"]
s12_53 = np.sum(t_WB_2012)
s12_53
Out[3736]:
In [3737]:
t_ANm_2012 = np.sum(df_st_AN_2012.where(df_st_AN_2012["Gender"]=="Male")["Total"])
t_ANm_2012
Out[3737]:
In [3738]:
t_ANfm_2012 = np.sum(df_st_AN_2012.where(df_st_AN_2012["Gender"]=="Female")["Total"])
t_ANfm_2012
Out[3738]:
In [3739]:
t_APm_2012 = np.sum(df_st_AP_2012.where(df_st_AP_2012["Gender"]=="Male")["Total"])
t_APm_2012
Out[3739]:
In [3740]:
t_APfm_2012 = np.sum(df_st_AP_2012.where(df_st_AP_2012["Gender"]=="Female")["Total"])
t_APfm_2012
Out[3740]:
In [3741]:
t_ARPm_2012 = np.sum(df_st_ARP_2012.where(df_st_ARP_2012["Gender"]=="Male")["Total"])
t_ARPm_2012
Out[3741]:
In [3742]:
t_ARPfm_2012 = np.sum(df_st_ARP_2012.where(df_st_ARP_2012["Gender"]=="Female")["Total"])
t_ARPfm_2012
Out[3742]:
In [3743]:
t_ASm_2012 = np.sum(df_st_AS_2012.where(df_st_AS_2012["Gender"]=="Male")["Total"])
t_ASm_2012
Out[3743]:
In [3744]:
t_ASfm_2012 = np.sum(df_st_AS_2012.where(df_st_AS_2012["Gender"]=="Female")["Total"])
t_ASfm_2012
Out[3744]:
In [3745]:
t_BHm_2012 = np.sum(df_st_BH_2012.where(df_st_BH_2012["Gender"]=="Male")["Total"])
t_BHm_2012
Out[3745]:
In [3746]:
t_BHfm_2012 = np.sum(df_st_BH_2012.where(df_st_BH_2012["Gender"]=="Female")["Total"])
t_BHfm_2012
Out[3746]:
In [3747]:
t_CHm_2012 = np.sum(df_st_CH_2012.where(df_st_CH_2012["Gender"]=="Male")["Total"])
t_CHm_2012
Out[3747]:
In [3748]:
t_CHfm_2012 = np.sum(df_st_CH_2012.where(df_st_CH_2012["Gender"]=="Female")["Total"])
t_CHfm_2012
Out[3748]:
In [3749]:
t_CTm_2012 = np.sum(df_st_CT_2012.where(df_st_CT_2012["Gender"]=="Male")["Total"])
t_CTm_2012
Out[3749]:
In [3750]:
t_CTfm_2012 = np.sum(df_st_CT_2012.where(df_st_CT_2012["Gender"]=="Female")["Total"])
t_CTfm_2012
Out[3750]:
In [3751]:
t_DNm_2012 = np.sum(df_st_DN_2012.where(df_st_DN_2012["Gender"]=="Male")["Total"])
t_DNm_2012
Out[3751]:
In [3752]:
t_DNfm_2012 = np.sum(df_st_DN_2012.where(df_st_DN_2012["Gender"]=="Female")["Total"])
t_DNfm_2012
Out[3752]:
In [3753]:
t_DDm_2012 = np.sum(df_st_DD_2012.where(df_st_DD_2012["Gender"]=="Male")["Total"])
t_DDm_2012
Out[3753]:
In [3754]:
t_DDfm_2012 = np.sum(df_st_DD_2012.where(df_st_DD_2012["Gender"]=="Female")["Total"])
t_DDfm_2012
Out[3754]:
In [3755]:
t_DLm_2012 = np.sum(df_st_DL_2012.where(df_st_DL_2012["Gender"]=="Male")["Total"])
t_DLm_2012
Out[3755]:
In [3756]:
t_DLfm_2012 = np.sum(df_st_DL_2012.where(df_st_DL_2012["Gender"]=="Female")["Total"])
t_DLfm_2012
Out[3756]:
In [3757]:
t_GOAm_2012 = np.sum(df_st_GOA_2012.where(df_st_GOA_2012["Gender"]=="Male")["Total"])
t_GOAm_2012
Out[3757]:
In [3758]:
t_GOAfm_2012 = np.sum(df_st_GOA_2012.where(df_st_GOA_2012["Gender"]=="Female")["Total"])
t_GOAfm_2012
Out[3758]:
In [3759]:
t_GJm_2012 = np.sum(df_st_GJ_2012.where(df_st_GJ_2012["Gender"]=="Male")["Total"])
t_GJm_2012
Out[3759]:
In [3760]:
t_GJfm_2012 = np.sum(df_st_GJ_2012.where(df_st_GJ_2012["Gender"]=="Female")["Total"])
t_GJfm_2012
Out[3760]:
In [3761]:
t_HRm_2012 = np.sum(df_st_HR_2012.where(df_st_HR_2012["Gender"]=="Male")["Total"])
t_HRm_2012
Out[3761]:
In [3762]:
t_HRfm_2012 = np.sum(df_st_HR_2012.where(df_st_HR_2012["Gender"]=="Female")["Total"])
t_HRfm_2012
Out[3762]:
In [3763]:
t_HPm_2012 = np.sum(df_st_HP_2012.where(df_st_HP_2012["Gender"]=="Male")["Total"])
t_HPm_2012
Out[3763]:
In [3764]:
t_HPfm_2012 = np.sum(df_st_HP_2012.where(df_st_HP_2012["Gender"]=="Female")["Total"])
t_HPfm_2012
Out[3764]:
In [3765]:
t_JKm_2012 = np.sum(df_st_JK_2012.where(df_st_JK_2012["Gender"]=="Male")["Total"])
t_JKm_2012
Out[3765]:
In [3766]:
t_JKfm_2012 = np.sum(df_st_JK_2012.where(df_st_JK_2012["Gender"]=="Female")["Total"])
t_JKfm_2012
Out[3766]:
In [3767]:
t_JHm_2012 = np.sum(df_st_JH_2012.where(df_st_JH_2012["Gender"]=="Male")["Total"])
t_JHm_2012
Out[3767]:
In [3768]:
t_JHfm_2012 = np.sum(df_st_JH_2012.where(df_st_JH_2012["Gender"]=="Female")["Total"])
t_JHfm_2012
Out[3768]:
In [3769]:
t_KNm_2012 = np.sum(df_st_KN_2012.where(df_st_KN_2012["Gender"]=="Male")["Total"])
t_KNm_2012
Out[3769]:
In [3770]:
t_KNfm_2012 = np.sum(df_st_KN_2012.where(df_st_KN_2012["Gender"]=="Female")["Total"])
t_KNfm_2012
Out[3770]:
In [3771]:
t_KERm_2012 = np.sum(df_st_KER_2012.where(df_st_KER_2012["Gender"]=="Male")["Total"])
t_KERm_2012
Out[3771]:
In [3772]:
t_KERfm_2012 = np.sum(df_st_KER_2012.where(df_st_KER_2012["Gender"]=="Female")["Total"])
t_KERfm_2012
Out[3772]:
In [3831]:
t_LDm_2012 = np.sum(df_st_LD_2012.where(df_st_LD_2012["Gender"]=="Male")["Total"])
t_LDm_2012
Out[3831]:
In [3832]:
t_LDfm_2012 = np.sum(df_st_LD_2012.where(df_st_LD_2012["Gender"]=="Female")["Total"])
t_LDfm_2012
Out[3832]:
In [3833]:
t_MPm_2012 = np.sum(df_st_MP_2012.where(df_st_MP_2012["Gender"]=="Male")["Total"])
t_MPm_2012
Out[3833]:
In [3834]:
t_MPfm_2012 = np.sum(df_st_MP_2012.where(df_st_MP_2012["Gender"]=="Female")["Total"])
t_MPfm_2012
Out[3834]:
In [3835]:
t_MHm_2012 = np.sum(df_st_MH_2012.where(df_st_MH_2012["Gender"]=="Male")["Total"])
t_MHm_2012
Out[3835]:
In [3836]:
t_MHfm_2012 = np.sum(df_st_MH_2012.where(df_st_MH_2012["Gender"]=="Female")["Total"])
t_MHfm_2012
Out[3836]:
In [3837]:
t_MNm_2012 = np.sum(df_st_MN_2012.where(df_st_MN_2012["Gender"]=="Male")["Total"])
t_MNm_2012
Out[3837]:
In [3838]:
t_MNfm_2012 = np.sum(df_st_MN_2012.where(df_st_MN_2012["Gender"]=="Female")["Total"])
t_MNfm_2012
Out[3838]:
In [3839]:
t_MGm_2012 = np.sum(df_st_MG_2012.where(df_st_MG_2012["Gender"]=="Male")["Total"])
t_MGm_2012
Out[3839]:
In [3840]:
t_MGfm_2012 = np.sum(df_st_MG_2012.where(df_st_MG_2012["Gender"]=="Female")["Total"])
t_MGfm_2012
Out[3840]:
In [3841]:
t_MZm_2012 = np.sum(df_st_MZ_2012.where(df_st_MZ_2012["Gender"]=="Male")["Total"])
t_MZm_2012
Out[3841]:
In [3842]:
t_MZfm_2012 = np.sum(df_st_MZ_2012.where(df_st_MZ_2012["Gender"]=="Female")["Total"])
t_MZfm_2012
Out[3842]:
In [3843]:
t_NGm_2012 = np.sum(df_st_NG_2012.where(df_st_NG_2012["Gender"]=="Male")["Total"])
t_NGm_2012
Out[3843]:
In [3844]:
t_NGfm_2012 = np.sum(df_st_NG_2012.where(df_st_NG_2012["Gender"]=="Female")["Total"])
t_MGfm_2012
Out[3844]:
In [3845]:
t_ODm_2012 = np.sum(df_st_OD_2012.where(df_st_OD_2012["Gender"]=="Male")["Total"])
t_ODm_2012
Out[3845]:
In [3846]:
t_ODfm_2012 = np.sum(df_st_OD_2012.where(df_st_OD_2012["Gender"]=="Female")["Total"])
t_ODfm_2012
Out[3846]:
In [3847]:
t_PDm_2012 = np.sum(df_st_PD_2012.where(df_st_PD_2012["Gender"]=="Male")["Total"])
t_PDm_2012
Out[3847]:
In [3848]:
t_PDfm_2012 = np.sum(df_st_PD_2012.where(df_st_PD_2012["Gender"]=="Female")["Total"])
t_PDfm_2012
Out[3848]:
In [3849]:
t_PBm_2012 = np.sum(df_st_PB_2012.where(df_st_PB_2012["Gender"]=="Male")["Total"])
t_PBm_2012
Out[3849]:
In [3850]:
t_PBfm_2012 = np.sum(df_st_PB_2012.where(df_st_PB_2012["Gender"]=="Female")["Total"])
t_PBfm_2012
Out[3850]:
In [3851]:
t_RJm_2012 = np.sum(df_st_RJ_2012.where(df_st_RJ_2012["Gender"]=="Male")["Total"])
t_RJm_2012
Out[3851]:
In [3852]:
t_RJfm_2012 = np.sum(df_st_RJ_2012.where(df_st_RJ_2012["Gender"]=="Female")["Total"])
t_RJfm_2012
Out[3852]:
In [3853]:
t_SMm_2012 = np.sum(df_st_SM_2012.where(df_st_SM_2012["Gender"]=="Male")["Total"])
t_SMm_2012
Out[3853]:
In [3854]:
t_SMfm_2012 = np.sum(df_st_SM_2012.where(df_st_SM_2012["Gender"]=="Female")["Total"])
t_SMfm_2012
Out[3854]:
In [3855]:
t_TNm_2012 = np.sum(df_st_TN_2012.where(df_st_TN_2012["Gender"]=="Male")["Total"])
t_TNm_2012
Out[3855]:
In [3856]:
t_TNfm_2012 = np.sum(df_st_TN_2012.where(df_st_TN_2012["Gender"]=="Female")["Total"])
t_TNfm_2012
Out[3856]:
In [3857]:
t_TPm_2012 = np.sum(df_st_TP_2012.where(df_st_TP_2012["Gender"]=="Male")["Total"])
t_TPm_2012
Out[3857]:
In [3858]:
t_TPfm_2012 = np.sum(df_st_TP_2012.where(df_st_TP_2012["Gender"]=="Female")["Total"])
t_TPfm_2012
Out[3858]:
In [3859]:
t_UPm_2012 = np.sum(df_st_UP_2012.where(df_st_UP_2012["Gender"]=="Male")["Total"])
t_UPm_2012
Out[3859]:
In [3860]:
t_UPfm_2012 = np.sum(df_st_UP_2012.where(df_st_UP_2012["Gender"]=="Female")["Total"])
t_UPfm_2012
Out[3860]:
In [3861]:
t_UKm_2012 = np.sum(df_st_UK_2012.where(df_st_UK_2012["Gender"]=="Male")["Total"])
t_UKm_2012
Out[3861]:
In [3862]:
t_UKfm_2012 = np.sum(df_st_UK_2012.where(df_st_UK_2012["Gender"]=="Female")["Total"])
t_UKfm_2012
Out[3862]:
In [3863]:
t_WBm_2012 = np.sum(df_st_WB_2012.where(df_st_WB_2012["Gender"]=="Male")["Total"])
t_WBm_2012
Out[3863]:
In [3864]:
t_WBfm_2012 = np.sum(df_st_WB_2012.where(df_st_WB_2012["Gender"]=="Female")["Total"])
t_WBfm_2012
Out[3864]:
In [3865]:
l_2012=list(df_2012.loc[:,"Type"].unique())
l_2012.sort()
print(l_2012,end=" ")
In [3866]:
cause_l_2012=[]
for i in l_2012:
rough= np.sum(df_2012.where(df_2012["Type"]==i)["Total"])
cause_l_2012.append(rough)
print(cause_l_2012,end=" ")
In [3867]:
x1_2012 = set(df_2012.loc[:,"State"])
x1_2012
len(x1_2012)
Out[3867]:
In [3868]:
cause_df_2012 = pd.DataFrame(cause_l_2012,l_2012)
cause_df_2012.reset_index()
Out[3868]:
In [3869]:
cause_df_2012.plot.bar(stacked=True,figsize=(50,20))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("Causes",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths due to Different Causes in 2012",fontsize=55)
plt.legend(["Death Count"], prop={"size":30})
Out[3869]:
In [3870]:
s_2012=list(df_2012.loc[:,"State"].unique())
s_2012.sort()
print(s_2012,end=" ")
In [3871]:
state_s_2012 = [s12_19,s12_20,s12_21,s12_22,s12_23,s12_24,s12_25,s12_26,s12_27,s12_28,s12_29,s12_30,s12_31,s12_32,s12_33,s12_34,
s12_35,s12_36,s12_37,s12_38,s12_39,s12_40,s12_41,s12_42,s12_43,s12_44,s12_45,s12_46,s12_47,s12_48,s12_49,s12_50,
s12_51,s12_52,s12_53]
print(state_s_2012,end=" ")
In [3872]:
state_m_2012=[t_ANm_2012, t_APm_2012, t_ARPm_2012, t_ASm_2012, t_BHm_2012, t_CHm_2012, t_CTm_2012, t_DNm_2012,
t_DDm_2012, t_DLm_2012, t_GOAm_2012, t_GJm_2012, t_HRm_2012, t_HPm_2012,t_JKm_2012, t_JHm_2012,
t_KNm_2012, t_KERm_2012, t_LDm_2012,t_MPm_2012,t_MHm_2012,t_MNm_2012,t_MGm_2012,t_MZm_2012,
t_NGm_2012, t_ODm_2012, t_PDm_2012,t_PBm_2012,t_RJm_2012,t_SMm_2012,t_TNm_2012,t_TPm_2012,
t_UPm_2012, t_UKm_2012, t_WBm_2012]
print(state_m_2012,end=" ")
In [3873]:
state_fm_2012=[t_ANfm_2012, t_APfm_2012, t_ARPfm_2012, t_ASfm_2012, t_BHfm_2012, t_CHfm_2012, t_CTfm_2012, t_DNfm_2012,
t_DDfm_2012, t_DLfm_2012, t_GOAfm_2012, t_GJfm_2012, t_HRfm_2012, t_HPfm_2012,t_JKfm_2012, t_JHfm_2012,
t_KNfm_2012, t_KERfm_2012, t_LDfm_2012,t_MPfm_2012,t_MHfm_2012,t_MNfm_2012,t_MGfm_2012,t_MZfm_2012,
t_NGfm_2012, t_ODfm_2012, t_PDfm_2012,t_PBfm_2012,t_RJfm_2012,t_SMfm_2012,t_TNfm_2012,t_TPfm_2012,
t_UPfm_2012, t_UKfm_2012, t_WBfm_2012]
print(state_fm_2012,end=" ")
In [3874]:
df_state_2012 = {
"State":['A & N ISLANDS', 'ANDHRA PRADESH', 'ARUNACHAL PRADESH', 'ASSAM', 'BIHAR', 'CHANDIGARH', 'CHHATTISGARH',
'D & N HAVELI', 'DAMAN & DIU', 'DELHI (UT)', 'GOA', 'GUJARAT', 'HARYANA', 'HIMACHAL PRADESH', 'JAMMU & KASHMIR',
'JHARKHAND', 'KARNATAKA', 'KERALA', 'LAKSHADWEEP', 'MADHYA PRADESH', 'MAHARASHTRA', 'MANIPUR', 'MEGHALAYA',
'MIZORAM', 'NAGALAND', 'ODISHA', 'PUDUCHERRY', 'PUNJAB', 'RAJASTHAN', 'SIKKIM', 'TAMIL NADU', 'TRIPURA',
'UTTAR PRADESH', 'UTTARAKHAND', 'WEST BENGAL'],
"Total":[s12_19,s12_20,s12_21,s12_22,s12_23,s12_24,s12_25,s12_26,s12_27,s12_28,s12_29,s12_30,s12_31,s12_32,s12_33,s12_34,
s12_35,s12_36,s12_37,s12_38,s12_39,s12_40,s12_41,s12_42,s12_43,s12_44,s12_45,s12_46,s12_47,s12_48,s12_49,s12_50,
s12_51,s12_52,s12_53],
"Male":[t_ANm_2012, t_APm_2012, t_ARPm_2012, t_ASm_2012, t_BHm_2012, t_CHm_2012, t_CTm_2012, t_DNm_2012,
t_DDm_2012, t_DLm_2012, t_GOAm_2012, t_GJm_2012, t_HRm_2012, t_HPm_2012,t_JKm_2012, t_JHm_2012,
t_KNm_2012, t_KERm_2012, t_LDm_2012,t_MPm_2012,t_MHm_2012,t_MNm_2012,t_MGm_2012,t_MZm_2012,
t_NGm_2012, t_ODm_2012, t_PDm_2012,t_PBm_2012,t_RJm_2012,t_SMm_2012,t_TNm_2012,t_TPm_2012,
t_UPm_2012, t_UKm_2012, t_WBm_2012],
"Female":[t_ANfm_2012, t_APfm_2012, t_ARPfm_2012, t_ASfm_2012, t_BHfm_2012, t_CHfm_2012, t_CTfm_2012, t_DNfm_2012,
t_DDfm_2012, t_DLfm_2012, t_GOAfm_2012, t_GJfm_2012, t_HRfm_2012, t_HPfm_2012,t_JKfm_2012, t_JHfm_2012,
t_KNfm_2012, t_KERfm_2012, t_LDfm_2012,t_MPfm_2012,t_MHfm_2012,t_MNfm_2012,t_MGfm_2012,t_MZfm_2012,
t_NGfm_2012, t_ODfm_2012, t_PDfm_2012,t_PBfm_2012,t_RJfm_2012,t_SMfm_2012,t_TNfm_2012,t_TPfm_2012,
t_UPfm_2012, t_UKfm_2012, t_WBfm_2012]
}
df_All_st_2012 = pd.DataFrame(df_state_2012)
df_All_st_2012
Out[3874]:
In [3875]:
pivot_2012 = pd.pivot_table(df_All_st_2012, values=None, index='State', columns=None,
fill_value=None, margins=False, dropna=True, margins_name='All', observed=False)
pivot_2012
Out[3875]:
In [3876]:
pivot_2012.plot(kind='bar',figsize=(60,30))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("States",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths of Males And Females In Different State",fontsize=55)
plt.legend(["Female","Male","Total"], prop={"size":30})
plt.show
Out[3876]:
In [3877]:
cause_l_percent_2012=[]
for i in cause_l_2012:
p=i/np.sum(cause_l_2012)
avg_2012_cause=p*100
cause_l_percent_2012.append(avg_2012_cause)
print(cause_l_percent_2012,end=" ")
In [3878]:
np.max(cause_l_percent_2012) #Married
Out[3878]:
In [3879]:
np.min(cause_l_percent_2012) #Ideological Causes/Hero Worshipping
Out[3879]:
In [3880]:
state_s_2012_percent=[]
tot=np.sum(state_s_2012)
for i in state_s_2012:
xy=i/tot
avg_state_2012=xy*100
state_s_2012_percent.append(avg_state_2012)
print(state_s_2012_percent,end=" ")
In [3881]:
np.max(state_s_2012_percent) #Maharashtra
Out[3881]:
In [3882]:
np.min(state_s_2012_percent) #Lakshdweep
Out[3882]:
In [3883]:
l_age_grp_2012=[s12_4,s12_5,s12_6,s12_7,s12_8,s12_54]
l_age_grp_2012
Out[3883]:
In [3884]:
l_age_grp_2012_percent=[]
for i in l_age_grp_2012:
yz=i/s12_1
avg_2012_age_grp=yz*100
l_age_grp_2012_percent.append(avg_2012_age_grp)
l_age_grp_2012_percent
Out[3884]:
In [3885]:
np.max(l_age_grp_2012_percent) #0-100+
Out[3885]:
In [3886]:
np.min(l_age_grp_2012_percent) #0-14
Out[3886]:
In [3887]:
total_male_percent_2012= (s12_2/s12_1)*100
total_male_percent_2012
Out[3887]:
In [3888]:
total_female_percent_2012= (s12_3/s12_1)*100
total_female_percent_2012
Out[3888]:
Total death-count yearwise¶
In [3900]:
dict_yr = {
"Years":[2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012],
"Male":[s1_2,s2_2,s3_2,s4_2,s5_2,s6_2,s7_2,s8_2,s9_2,s10_2,s11_2,s12_2],
"Female":[s1_3,s2_3,s3_3,s4_3,s5_3,s6_3,s7_3,s8_3,s9_3,s10_3,s11_3,s12_3],
"Total": [s1_1,s2_1,s3_1,s4_1,s5_1,s6_1,s7_1,s8_1,s9_1,s10_1,s11_1,s12_1]
}
all_df = pd.DataFrame(dict_yr)
all_df
Out[3900]:
In [3901]:
pivot_allyr = pd.pivot_table(all_df, values=None, index="Years", columns=None,
fill_value=None, margins=False, dropna=True, margins_name='All', observed=False)
pivot_allyr
Out[3901]:
In [3905]:
pivot_allyr.plot(kind='bar',figsize=(10,8))
plt.xlabel("Years",fontsize=15)
plt.ylabel("Death Count",fontsize=12)
Out[3905]:
agegrp wise deathcounts¶
In [3906]:
df_agegrp_allyr = {
"Years":[2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012],
"0-14":[s1_4,s2_4,s3_4,s4_4,s5_4,s6_4,s7_4,s8_4,s9_4,s10_4,s11_4,s12_4],
"15-29":[s1_5,s2_5,s3_5,s4_5,s5_5,s6_5,s7_5,s8_5,s9_5,s10_5,s11_5,s12_5],
"30-44":[s1_6,s2_6,s3_6,s4_6,s5_6,s6_6,s7_6,s8_6,s9_6,s10_6,s11_6,s12_6],
"45-59":[s1_7,s2_7,s3_7,s4_7,s5_7,s6_7,s7_7,s8_7,s9_7,s10_7,s11_7,s12_7],
"60+":[s1_8,s2_8,s3_8,s4_8,s5_8,s6_8,s7_8,s8_8,s9_8,s10_8,s11_8,s12_8],
"0-100+":[s1_54,s2_54,s3_54,s4_54,s5_54,s6_54,s7_54,s8_54,s9_54,s10_54,s11_54,s12_54]
}
df_all_agegrp_allyr=pd.DataFrame(df_agegrp_allyr)
df_all_agegrp_allyr
Out[3906]:
In [3896]:
pivot_agegrp_all = pd.pivot_table(df_all_agegrp_allyr, values=None, index='Years', columns=None,
fill_value=None, margins=False, dropna=True, margins_name='All', observed=False)
pivot_agegrp_all
Out[3896]:
In [3898]:
pivot_agegrp_all.plot(kind='bar',figsize=(60,30))
plt.tick_params(axis='y', labelsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.xlabel("Years",fontsize=40)
plt.ylabel("Death Count",fontsize=40)
plt.title("Number of Deaths of Diffrent AgeGroups Year-Wise",fontsize=55)
plt.legend(["0-100+","0-14","15-29","30-44","45-59","60+"], prop={"size":30})
plt.show
Out[3898]:
In [ ]: